Overview

Dataset statistics

Number of variables69
Number of observations4964
Missing cells388
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory552.0 B

Variable types

Numeric19
Categorical39
Boolean11

Warnings

EEA_PolicyYear has constant value "2006" Constant
Policy_Zip_Code_Garaging_Location has a high cardinality: 699 distinct values High cardinality
Vehicle_Make_Description has a high cardinality: 1166 distinct values High cardinality
PolicyNo is highly correlated with EEA_Policy_TenureHigh correlation
Vehicle_Make_Year is highly correlated with Vehicle_Age_In_Years and 2 other fieldsHigh correlation
Vehicle_Age_In_Years is highly correlated with Vehicle_Make_Year and 2 other fieldsHigh correlation
Vehicle_Collision_Coverage_Deductible is highly correlated with Vehicle_Make_Year and 2 other fieldsHigh correlation
Driver_Total is highly correlated with Driver_Total_Married and 1 other fieldsHigh correlation
Driver_Total_Male is highly correlated with Driver_Total_FemaleHigh correlation
Driver_Total_Female is highly correlated with Driver_Total_MaleHigh correlation
Driver_Total_Single is highly correlated with Driver_Total_MarriedHigh correlation
Driver_Total_Married is highly correlated with Driver_Total and 3 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Self is highly correlated with Driver_Total_Related_To_Insured_SpouseHigh correlation
Driver_Total_Related_To_Insured_Spouse is highly correlated with Driver_Total_Married and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Child is highly correlated with Driver_Total_Teenager_Age_15_19High correlation
Driver_Total_Licensed_In_State is highly correlated with Driver_Total and 1 other fieldsHigh correlation
Driver_Minimum_Age is highly correlated with Driver_Maximum_Age and 1 other fieldsHigh correlation
Driver_Maximum_Age is highly correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Driver_Total_Teenager_Age_15_19 is highly correlated with Driver_Total_Related_To_Insured_ChildHigh correlation
Driver_Total_Upper_Senior_Ages_70_plus is highly correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
EEA_Policy_Tenure is highly correlated with PolicyNoHigh correlation
Annual_Premium is highly correlated with Vehicle_Make_Year and 2 other fieldsHigh correlation
Loss_Amount is highly correlated with Severity and 1 other fieldsHigh correlation
Severity is highly correlated with Loss_Amount and 1 other fieldsHigh correlation
Loss_Ratio is highly correlated with Loss_Amount and 1 other fieldsHigh correlation
PolicyNo is highly correlated with EEA_Policy_TenureHigh correlation
Vehicle_Make_Year is highly correlated with Vehicle_Symbol and 3 other fieldsHigh correlation
Vehicle_Symbol is highly correlated with Vehicle_Make_Year and 1 other fieldsHigh correlation
Vehicle_Age_In_Years is highly correlated with Vehicle_Make_Year and 2 other fieldsHigh correlation
Vehicle_Collision_Coverage_Deductible is highly correlated with Vehicle_Make_Year and 2 other fieldsHigh correlation
Driver_Total is highly correlated with Driver_Total_Licensed_In_StateHigh correlation
Driver_Total_Male is highly correlated with Driver_Total_FemaleHigh correlation
Driver_Total_Female is highly correlated with Driver_Total_MaleHigh correlation
Driver_Total_Single is highly correlated with Driver_Total_MarriedHigh correlation
Driver_Total_Married is highly correlated with Driver_Total_Single and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Self is highly correlated with Driver_Total_Related_To_Insured_SpouseHigh correlation
Driver_Total_Related_To_Insured_Spouse is highly correlated with Driver_Total_Married and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Child is highly correlated with Driver_Total_Teenager_Age_15_19High correlation
Driver_Total_Licensed_In_State is highly correlated with Driver_TotalHigh correlation
Driver_Minimum_Age is highly correlated with Driver_Maximum_AgeHigh correlation
Driver_Maximum_Age is highly correlated with Driver_Minimum_AgeHigh correlation
Driver_Total_Teenager_Age_15_19 is highly correlated with Driver_Total_Related_To_Insured_ChildHigh correlation
EEA_Policy_Tenure is highly correlated with PolicyNoHigh correlation
Annual_Premium is highly correlated with Vehicle_Make_Year and 3 other fieldsHigh correlation
Claim_Count is highly correlated with Loss_Amount and 3 other fieldsHigh correlation
Loss_Amount is highly correlated with Claim_Count and 3 other fieldsHigh correlation
Frequency is highly correlated with Claim_Count and 3 other fieldsHigh correlation
Severity is highly correlated with Claim_Count and 3 other fieldsHigh correlation
Loss_Ratio is highly correlated with Claim_Count and 3 other fieldsHigh correlation
PolicyNo is highly correlated with EEA_Policy_TenureHigh correlation
Vehicle_Make_Year is highly correlated with Vehicle_Age_In_Years and 2 other fieldsHigh correlation
Vehicle_Age_In_Years is highly correlated with Vehicle_Make_Year and 1 other fieldsHigh correlation
Vehicle_Collision_Coverage_Deductible is highly correlated with Vehicle_Make_Year and 2 other fieldsHigh correlation
Driver_Total is highly correlated with Driver_Total_Licensed_In_StateHigh correlation
Driver_Total_Male is highly correlated with Driver_Total_FemaleHigh correlation
Driver_Total_Female is highly correlated with Driver_Total_MaleHigh correlation
Driver_Total_Single is highly correlated with Driver_Total_MarriedHigh correlation
Driver_Total_Married is highly correlated with Driver_Total_Single and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Self is highly correlated with Driver_Total_Related_To_Insured_SpouseHigh correlation
Driver_Total_Related_To_Insured_Spouse is highly correlated with Driver_Total_Married and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Child is highly correlated with Driver_Total_Teenager_Age_15_19High correlation
Driver_Total_Licensed_In_State is highly correlated with Driver_TotalHigh correlation
Driver_Minimum_Age is highly correlated with Driver_Maximum_AgeHigh correlation
Driver_Maximum_Age is highly correlated with Driver_Minimum_AgeHigh correlation
Driver_Total_Teenager_Age_15_19 is highly correlated with Driver_Total_Related_To_Insured_ChildHigh correlation
EEA_Policy_Tenure is highly correlated with PolicyNoHigh correlation
Annual_Premium is highly correlated with Vehicle_Make_Year and 1 other fieldsHigh correlation
Claim_Count is highly correlated with Loss_Amount and 3 other fieldsHigh correlation
Loss_Amount is highly correlated with Claim_Count and 3 other fieldsHigh correlation
Frequency is highly correlated with Claim_Count and 3 other fieldsHigh correlation
Severity is highly correlated with Claim_Count and 3 other fieldsHigh correlation
Loss_Ratio is highly correlated with Claim_Count and 3 other fieldsHigh correlation
Vehicle_Collision_Coverage_Indicator is highly correlated with EEA_Packaged_Policy_Indicator and 8 other fieldsHigh correlation
Driver_Total_College_Ages_20_23 is highly correlated with Driver_Minimum_Age and 2 other fieldsHigh correlation
EEA_Packaged_Policy_Indicator is highly correlated with Vehicle_Collision_Coverage_Indicator and 4 other fieldsHigh correlation
EEA_Agency_Type is highly correlated with EEA_Policy_Zip_Code_3High correlation
Vehicle_Symbol is highly correlated with Vehicle_Collision_Coverage_Indicator and 3 other fieldsHigh correlation
Driver_Minimum_Age is highly correlated with Driver_Total_College_Ages_20_23 and 13 other fieldsHigh correlation
Vehicle_Passive_Restraint is highly correlated with Vehicle_Make_Year and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Spouse is highly correlated with Driver_Total_Married and 4 other fieldsHigh correlation
Driver_Total_Upper_Senior_Ages_70_plus is highly correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Loss_Amount is highly correlated with Loss_Ratio and 1 other fieldsHigh correlation
Driver_Total_Licensed_In_State is highly correlated with Driver_Total_Married and 1 other fieldsHigh correlation
Driver_Total_Senior_Ages_65_69 is highly correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Vehicle_Bodily_Injury_Limit is highly correlated with Vehicle_Physical_Damage_Limit and 1 other fieldsHigh correlation
Vehicle_Youthful_Driver_Training_Code is highly correlated with Driver_Minimum_Age and 5 other fieldsHigh correlation
Loss_Ratio is highly correlated with Loss_Amount and 2 other fieldsHigh correlation
EEA_Full_Coverage_Indicator is highly correlated with Vehicle_Collision_Coverage_Indicator and 8 other fieldsHigh correlation
Vehicle_Physical_Damage_Limit is highly correlated with Vehicle_Bodily_Injury_Limit and 1 other fieldsHigh correlation
Vehicle_Anti_Theft_Device is highly correlated with Vehicle_Collision_Coverage_Indicator and 4 other fieldsHigh correlation
Driver_Total_Adult_Ages_50_64 is highly correlated with Driver_Minimum_Age and 5 other fieldsHigh correlation
Severity is highly correlated with Loss_Amount and 1 other fieldsHigh correlation
EEA_Policy_Tenure is highly correlated with PolicyNo and 1 other fieldsHigh correlation
Driver_Total_Married is highly correlated with Driver_Total_Related_To_Insured_Spouse and 8 other fieldsHigh correlation
Driver_Total_Female is highly correlated with Driver_Total_Related_To_Insured_Spouse and 3 other fieldsHigh correlation
PolicyNo is highly correlated with EEA_Policy_Tenure and 2 other fieldsHigh correlation
Annual_Premium is highly correlated with Vehicle_Collision_Coverage_Indicator and 7 other fieldsHigh correlation
Driver_Total_Single is highly correlated with Driver_Minimum_Age and 4 other fieldsHigh correlation
Vehicle_Safe_Driver_Discount_Indicator is highly correlated with EEA_Policy_Tenure and 2 other fieldsHigh correlation
EEA_Multi_Auto_Policies_Indicator is highly correlated with EEA_Packaged_Policy_IndicatorHigh correlation
Policy_Billing_Code is highly correlated with Annual_Premium and 1 other fieldsHigh correlation
Driver_Total_Young_Adult_Ages_24_29 is highly correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Driver_Total is highly correlated with Driver_Total_Related_To_Insured_Spouse and 7 other fieldsHigh correlation
Driver_Total_Low_Middle_Adult_Ages_30_39 is highly correlated with Driver_Minimum_Age and 4 other fieldsHigh correlation
Vehicle_Annual_Miles is highly correlated with Vehicle_Days_Per_Week_DrivenHigh correlation
Vehicle_Make_Year is highly correlated with Vehicle_Collision_Coverage_Indicator and 5 other fieldsHigh correlation
Policy_Installment_Term is highly correlated with Annual_Premium and 1 other fieldsHigh correlation
Vehicle_Days_Per_Week_Driven is highly correlated with Vehicle_Annual_MilesHigh correlation
Vehicle_Miles_To_Work is highly correlated with Vehicle_UsageHigh correlation
Vehicle_Youthful_Driver_Indicator is highly correlated with Driver_Minimum_Age and 3 other fieldsHigh correlation
Vehicle_Youthful_Good_Student_Code is highly correlated with Vehicle_Youthful_Driver_Training_Code and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Self is highly correlated with Driver_Total_Related_To_Insured_Spouse and 2 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Child is highly correlated with Driver_Total_College_Ages_20_23 and 6 other fieldsHigh correlation
Frequency is highly correlated with Loss_RatioHigh correlation
EEA_Policy_Zip_Code_3 is highly correlated with EEA_Agency_Type and 1 other fieldsHigh correlation
Vehicle_Territory is highly correlated with EEA_Policy_Zip_Code_3High correlation
Driver_Total_Male is highly correlated with Driver_Total_Married and 3 other fieldsHigh correlation
Vehicle_Usage is highly correlated with Vehicle_Miles_To_WorkHigh correlation
Driver_Total_Teenager_Age_15_19 is highly correlated with Driver_Minimum_Age and 4 other fieldsHigh correlation
Vehicle_Driver_Points is highly correlated with Vehicle_Safe_Driver_Discount_IndicatorHigh correlation
Driver_Maximum_Age is highly correlated with Driver_Total_College_Ages_20_23 and 12 other fieldsHigh correlation
EEA_Prior_Bodily_Injury_Limit is highly correlated with Vehicle_Bodily_Injury_Limit and 1 other fieldsHigh correlation
Vehicle_Comprehensive_Coverage_Indicator is highly correlated with Vehicle_Comprehensive_Coverage_LimitHigh correlation
Vehicle_Number_Of_Drivers_Assigned is highly correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Vehicle_Collision_Coverage_Deductible is highly correlated with Vehicle_Collision_Coverage_Indicator and 2 other fieldsHigh correlation
Vehicle_Performance is highly correlated with Vehicle_SymbolHigh correlation
SYS_New_Business is highly correlated with PolicyNoHigh correlation
Vehicle_Age_In_Years is highly correlated with Vehicle_Collision_Coverage_Indicator and 4 other fieldsHigh correlation
EEA_Liability_Coverage_Only_Indicator is highly correlated with Vehicle_Collision_Coverage_Indicator and 5 other fieldsHigh correlation
Vehicle_Comprehensive_Coverage_Limit is highly correlated with Vehicle_Comprehensive_Coverage_IndicatorHigh correlation
Driver_Total_Middle_Adult_Ages_40_49 is highly correlated with Driver_Minimum_Age and 4 other fieldsHigh correlation
EEA_Multi_Auto_Policies_Indicator is highly correlated with EEA_PolicyYearHigh correlation
Policy_Billing_Code is highly correlated with Policy_Installment_Term and 1 other fieldsHigh correlation
Vehicle_Collision_Coverage_Indicator is highly correlated with EEA_Full_Coverage_Indicator and 2 other fieldsHigh correlation
Driver_Total_College_Ages_20_23 is highly correlated with Vehicle_Youthful_Driver_Indicator and 1 other fieldsHigh correlation
Driver_Total_Young_Adult_Ages_24_29 is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total is highly correlated with Driver_Total_Licensed_In_State and 2 other fieldsHigh correlation
EEA_Packaged_Policy_Indicator is highly correlated with EEA_Liability_Coverage_Only_Indicator and 1 other fieldsHigh correlation
Policy_Company is highly correlated with EEA_PolicyYearHigh correlation
EEA_Agency_Type is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Low_Middle_Adult_Ages_30_39 is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Annual_Miles is highly correlated with EEA_PolicyYearHigh correlation
Policy_Installment_Term is highly correlated with Policy_Billing_Code and 1 other fieldsHigh correlation
Vehicle_Days_Per_Week_Driven is highly correlated with EEA_PolicyYearHigh correlation
Policy_Method_Of_Payment is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Youthful_Driver_Indicator is highly correlated with Driver_Total_College_Ages_20_23 and 5 other fieldsHigh correlation
Vehicle_Passive_Restraint is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Youthful_Good_Student_Code is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Related_To_Insured_Self is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Related_To_Insured_Spouse is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Related_To_Insured_Child is highly correlated with Vehicle_Youthful_Driver_Indicator and 3 other fieldsHigh correlation
Driver_Total_Upper_Senior_Ages_70_plus is highly correlated with EEA_PolicyYearHigh correlation
EEA_Policy_Zip_Code_3 is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Male is highly correlated with Driver_Total_Female and 1 other fieldsHigh correlation
Driver_Total_Licensed_In_State is highly correlated with Driver_Total and 2 other fieldsHigh correlation
Driver_Total_Senior_Ages_65_69 is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Usage is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Teenager_Age_15_19 is highly correlated with Vehicle_Youthful_Driver_Indicator and 3 other fieldsHigh correlation
Vehicle_Bodily_Injury_Limit is highly correlated with EEA_Prior_Bodily_Injury_Limit and 2 other fieldsHigh correlation
Vehicle_Driver_Points is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Youthful_Driver_Training_Code is highly correlated with Vehicle_Youthful_Driver_Indicator and 3 other fieldsHigh correlation
SYS_Renewed is highly correlated with EEA_PolicyYearHigh correlation
Policy_Reinstatement_Fee_Indicator is highly correlated with EEA_PolicyYearHigh correlation
EEA_Prior_Bodily_Injury_Limit is highly correlated with Vehicle_Bodily_Injury_Limit and 2 other fieldsHigh correlation
Vehicle_Comprehensive_Coverage_Indicator is highly correlated with Vehicle_Bodily_Injury_Limit and 2 other fieldsHigh correlation
EEA_Full_Coverage_Indicator is highly correlated with Vehicle_Collision_Coverage_Indicator and 2 other fieldsHigh correlation
Vehicle_Number_Of_Drivers_Assigned is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Performance is highly correlated with EEA_PolicyYearHigh correlation
SYS_New_Business is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Anti_Theft_Device is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Adult_Ages_50_64 is highly correlated with EEA_PolicyYearHigh correlation
Driver_Total_Married is highly correlated with Driver_Total and 2 other fieldsHigh correlation
Driver_Total_Female is highly correlated with Driver_Total_Male and 1 other fieldsHigh correlation
Claim_Count is highly correlated with EEA_PolicyYearHigh correlation
EEA_Liability_Coverage_Only_Indicator is highly correlated with Vehicle_Collision_Coverage_Indicator and 3 other fieldsHigh correlation
Driver_Total_Single is highly correlated with Vehicle_Youthful_Driver_Indicator and 1 other fieldsHigh correlation
EEA_PolicyYear is highly correlated with EEA_Multi_Auto_Policies_Indicator and 46 other fieldsHigh correlation
Driver_Total_Middle_Adult_Ages_40_49 is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Safe_Driver_Discount_Indicator is highly correlated with EEA_PolicyYearHigh correlation
Vehicle_Bodily_Injury_Limit has 194 (3.9%) missing values Missing
EEA_Prior_Bodily_Injury_Limit has 194 (3.9%) missing values Missing
Loss_Amount is highly skewed (γ1 = 51.00506603) Skewed
Frequency is highly skewed (γ1 = 46.5164757) Skewed
Severity is highly skewed (γ1 = 51.69328358) Skewed
Loss_Ratio is highly skewed (γ1 = 40.7590306) Skewed
PolicyNo has unique values Unique
EEA_Policy_Tenure has 459 (9.2%) zeros Zeros
Loss_Amount has 4715 (95.0%) zeros Zeros
Frequency has 4715 (95.0%) zeros Zeros
Severity has 4715 (95.0%) zeros Zeros
Loss_Ratio has 4715 (95.0%) zeros Zeros

Reproduction

Analysis started2021-09-12 18:15:37.129951
Analysis finished2021-09-12 18:16:15.959715
Duration38.83 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

PolicyNo
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct4964
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315892133.9
Minimum164562033
Maximum381258900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:16.019606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum164562033
5-th percentile193920276
Q1286336490.2
median334182807.5
Q3361190102.8
95-th percentile376035380.9
Maximum381258900
Range216696867
Interquartile range (IQR)74853612.5

Descriptive statistics

Standard deviation56810824.85
Coefficient of variation (CV)0.1798424803
Kurtosis-0.01405011463
Mean315892133.9
Median Absolute Deviation (MAD)31945944
Skewness-1.008111599
Sum1.568088553 × 1012
Variance3.22746982 × 1015
MonotonicityStrictly increasing
2021-09-12T14:16:16.094173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3522751051
 
< 0.1%
3536862031
 
< 0.1%
3526248031
 
< 0.1%
3362147061
 
< 0.1%
3330915071
 
< 0.1%
3632565021
 
< 0.1%
2469444391
 
< 0.1%
2895162161
 
< 0.1%
3768882001
 
< 0.1%
3538398041
 
< 0.1%
Other values (4954)4954
99.8%
ValueCountFrequency (%)
1645620331
< 0.1%
1651191331
< 0.1%
1651662391
< 0.1%
1651988321
< 0.1%
1653195341
< 0.1%
1653550341
< 0.1%
1653862321
< 0.1%
1657086321
< 0.1%
1659511321
< 0.1%
1659710321
< 0.1%
ValueCountFrequency (%)
3812589001
< 0.1%
3811847001
< 0.1%
3811486001
< 0.1%
3811402001
< 0.1%
3811376001
< 0.1%
3810808001
< 0.1%
3810529001
< 0.1%
3810406001
< 0.1%
3810396001
< 0.1%
3810207001
< 0.1%

Policy_Company
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Standard
4679 
Preferred
 
285

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters44676
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 4679
94.3%
Preferred285
 
5.7%

Length

2021-09-12T14:16:16.212762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:16.242930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
standard4679
94.3%
preferred285
 
5.7%

Most occurring characters

ValueCountFrequency (%)
d9643
21.6%
a9358
20.9%
r5534
12.4%
S4679
10.5%
t4679
10.5%
n4679
10.5%
4679
10.5%
e855
 
1.9%
P285
 
0.6%
f285
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35033
78.4%
Uppercase Letter4964
 
11.1%
Space Separator4679
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d9643
27.5%
a9358
26.7%
r5534
15.8%
t4679
13.4%
n4679
13.4%
e855
 
2.4%
f285
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S4679
94.3%
P285
 
5.7%
Space Separator
ValueCountFrequency (%)
4679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39997
89.5%
Common4679
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
d9643
24.1%
a9358
23.4%
r5534
13.8%
S4679
11.7%
t4679
11.7%
n4679
11.7%
e855
 
2.1%
P285
 
0.7%
f285
 
0.7%
Common
ValueCountFrequency (%)
4679
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII44676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d9643
21.6%
a9358
20.9%
r5534
12.4%
S4679
10.5%
t4679
10.5%
n4679
10.5%
4679
10.5%
e855
 
1.9%
P285
 
0.6%
f285
 
0.6%

Policy_Installment_Term
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
6
4781 
12
 
183

Length

Max length2
Median length1
Mean length1.036865431
Min length1

Characters and Unicode

Total characters5147
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
64781
96.3%
12183
 
3.7%

Length

2021-09-12T14:16:16.558064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:16.597147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
64781
96.3%
12183
 
3.7%

Most occurring characters

ValueCountFrequency (%)
64781
92.9%
1183
 
3.6%
2183
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5147
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
64781
92.9%
1183
 
3.6%
2183
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common5147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
64781
92.9%
1183
 
3.6%
2183
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64781
92.9%
1183
 
3.6%
2183
 
3.6%

Policy_Billing_Code
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Direct Billed to Insured
4863 
Premium Finance
 
101

Length

Max length24
Median length24
Mean length23.81688155
Min length15

Characters and Unicode

Total characters118227
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect Billed to Insured
2nd rowDirect Billed to Insured
3rd rowDirect Billed to Insured
4th rowDirect Billed to Insured
5th rowDirect Billed to Insured

Common Values

ValueCountFrequency (%)
Direct Billed to Insured4863
98.0%
Premium Finance101
 
2.0%

Length

2021-09-12T14:16:16.693434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:16.731055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
to4863
24.7%
insured4863
24.7%
direct4863
24.7%
billed4863
24.7%
premium101
 
0.5%
finance101
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e14791
12.5%
14690
12.4%
i9928
 
8.4%
r9827
 
8.3%
t9726
 
8.2%
l9726
 
8.2%
d9726
 
8.2%
n5065
 
4.3%
c4964
 
4.2%
u4964
 
4.2%
Other values (9)24820
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter88746
75.1%
Uppercase Letter14791
 
12.5%
Space Separator14690
 
12.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e14791
16.7%
i9928
11.2%
r9827
11.1%
t9726
11.0%
l9726
11.0%
d9726
11.0%
n5065
 
5.7%
c4964
 
5.6%
u4964
 
5.6%
o4863
 
5.5%
Other values (3)5166
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
D4863
32.9%
B4863
32.9%
I4863
32.9%
P101
 
0.7%
F101
 
0.7%
Space Separator
ValueCountFrequency (%)
14690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin103537
87.6%
Common14690
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e14791
14.3%
i9928
9.6%
r9827
9.5%
t9726
9.4%
l9726
9.4%
d9726
9.4%
n5065
 
4.9%
c4964
 
4.8%
u4964
 
4.8%
D4863
 
4.7%
Other values (8)19957
19.3%
Common
ValueCountFrequency (%)
14690
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII118227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e14791
12.5%
14690
12.4%
i9928
 
8.4%
r9827
 
8.3%
t9726
 
8.2%
l9726
 
8.2%
d9726
 
8.2%
n5065
 
4.3%
c4964
 
4.2%
u4964
 
4.2%
Other values (9)24820
21.0%

Policy_Method_Of_Payment
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Installment
2572 
Pre-paid
2392 

Length

Max length11
Median length11
Mean length9.55439162
Min length8

Characters and Unicode

Total characters47428
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPre-paid
2nd rowPre-paid
3rd rowPre-paid
4th rowInstallment
5th rowPre-paid

Common Values

ValueCountFrequency (%)
Installment2572
51.8%
Pre-paid2392
48.2%

Length

2021-09-12T14:16:16.838612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:16.879719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
installment2572
51.8%
pre-paid2392
48.2%

Most occurring characters

ValueCountFrequency (%)
n5144
10.8%
t5144
10.8%
l5144
10.8%
e4964
10.5%
a4964
10.5%
I2572
 
5.4%
s2572
 
5.4%
m2572
 
5.4%
P2392
 
5.0%
r2392
 
5.0%
Other values (4)9568
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40072
84.5%
Uppercase Letter4964
 
10.5%
Dash Punctuation2392
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n5144
12.8%
t5144
12.8%
l5144
12.8%
e4964
12.4%
a4964
12.4%
s2572
6.4%
m2572
6.4%
r2392
6.0%
p2392
6.0%
i2392
6.0%
Uppercase Letter
ValueCountFrequency (%)
I2572
51.8%
P2392
48.2%
Dash Punctuation
ValueCountFrequency (%)
-2392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45036
95.0%
Common2392
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n5144
11.4%
t5144
11.4%
l5144
11.4%
e4964
11.0%
a4964
11.0%
I2572
 
5.7%
s2572
 
5.7%
m2572
 
5.7%
P2392
 
5.3%
r2392
 
5.3%
Other values (3)7176
15.9%
Common
ValueCountFrequency (%)
-2392
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII47428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n5144
10.8%
t5144
10.8%
l5144
10.8%
e4964
10.5%
a4964
10.5%
I2572
 
5.4%
s2572
 
5.4%
m2572
 
5.4%
P2392
 
5.0%
r2392
 
5.0%
Other values (4)9568
20.2%

Policy_Reinstatement_Fee_Indicator
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4385 
True
579 
ValueCountFrequency (%)
False4385
88.3%
True579
 
11.7%
2021-09-12T14:16:16.903053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Policy_Zip_Code_Garaging_Location
Categorical

HIGH CARDINALITY

Distinct699
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
43025
 
52
43050
 
48
42922
 
43
42878
 
43
43046
 
42
Other values (694)
4736 

Length

Max length7
Median length5
Mean length5.010878324
Min length5

Characters and Unicode

Total characters24874
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)2.6%

Sample

1st row42602
2nd row42857
3rd row42980
4th row43050
5th row42496

Common Values

ValueCountFrequency (%)
4302552
 
1.0%
4305048
 
1.0%
4292243
 
0.9%
4287843
 
0.9%
4304642
 
0.8%
4287340
 
0.8%
4306638
 
0.8%
4298837
 
0.7%
4246234
 
0.7%
4316933
 
0.7%
Other values (689)4554
91.7%

Length

2021-09-12T14:16:17.034984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4302552
 
1.0%
4305048
 
1.0%
4287843
 
0.9%
4292243
 
0.9%
4304642
 
0.8%
4287340
 
0.8%
4306638
 
0.8%
4298837
 
0.7%
4246234
 
0.7%
4316933
 
0.7%
Other values (689)4554
91.7%

Most occurring characters

ValueCountFrequency (%)
46605
26.6%
23537
14.2%
33067
12.3%
82189
 
8.8%
51768
 
7.1%
01645
 
6.6%
61630
 
6.6%
91574
 
6.3%
71420
 
5.7%
11250
 
5.0%
Other values (5)189
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24685
99.2%
Lowercase Letter162
 
0.7%
Uppercase Letter27
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
46605
26.8%
23537
14.3%
33067
12.4%
82189
 
8.9%
51768
 
7.2%
01645
 
6.7%
61630
 
6.6%
91574
 
6.4%
71420
 
5.8%
11250
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
n81
50.0%
k27
 
16.7%
o27
 
16.7%
w27
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
U27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24685
99.2%
Latin189
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
46605
26.8%
23537
14.3%
33067
12.4%
82189
 
8.9%
51768
 
7.2%
01645
 
6.7%
61630
 
6.6%
91574
 
6.4%
71420
 
5.8%
11250
 
5.1%
Latin
ValueCountFrequency (%)
n81
42.9%
U27
 
14.3%
k27
 
14.3%
o27
 
14.3%
w27
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46605
26.6%
23537
14.2%
33067
12.3%
82189
 
8.8%
51768
 
7.1%
01645
 
6.6%
61630
 
6.6%
91574
 
6.3%
71420
 
5.7%
11250
 
5.0%
Other values (5)189
 
0.8%

Vehicle_Territory
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.10374698
Minimum13
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:17.092128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20
Q130
median31
Q335
95-th percentile35
Maximum37
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.17778323
Coefficient of variation (CV)0.1343176831
Kurtosis6.114006456
Mean31.10374698
Median Absolute Deviation (MAD)1
Skewness-2.129050807
Sum154399
Variance17.45387272
MonotonicityNot monotonic
2021-09-12T14:16:17.147113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
312096
42.2%
351304
26.3%
30775
 
15.6%
27197
 
4.0%
32120
 
2.4%
37105
 
2.1%
1762
 
1.2%
2654
 
1.1%
1354
 
1.1%
2044
 
0.9%
Other values (6)153
 
3.1%
ValueCountFrequency (%)
1354
 
1.1%
1521
 
0.4%
1634
 
0.7%
1762
 
1.2%
1814
 
0.3%
1928
 
0.6%
2044
 
0.9%
2215
 
0.3%
2654
 
1.1%
27197
4.0%
ValueCountFrequency (%)
37105
 
2.1%
3641
 
0.8%
351304
26.3%
32120
 
2.4%
312096
42.2%
30775
 
15.6%
27197
 
4.0%
2654
 
1.1%
2215
 
0.3%
2044
 
0.9%

Vehicle_Make_Year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.703666
Minimum1939
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:17.212659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1939
5-th percentile1984
Q11993
median1998
Q32002
95-th percentile2005
Maximum2007
Range68
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.402684426
Coefficient of variation (CV)0.003707452713
Kurtosis4.022597618
Mean1996.703666
Median Absolute Deviation (MAD)4
Skewness-1.559335897
Sum9911637
Variance54.79973671
MonotonicityNot monotonic
2021-09-12T14:16:17.290294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000356
 
7.2%
1999314
 
6.3%
2002307
 
6.2%
2001305
 
6.1%
1997298
 
6.0%
1998297
 
6.0%
2004296
 
6.0%
2005281
 
5.7%
2003267
 
5.4%
1995253
 
5.1%
Other values (40)1990
40.1%
ValueCountFrequency (%)
19391
 
< 0.1%
19552
 
< 0.1%
19561
 
< 0.1%
19572
 
< 0.1%
19581
 
< 0.1%
19633
 
0.1%
19645
0.1%
19653
 
0.1%
196612
0.2%
19673
 
0.1%
ValueCountFrequency (%)
200744
 
0.9%
2006193
3.9%
2005281
5.7%
2004296
6.0%
2003267
5.4%
2002307
6.2%
2001305
6.1%
2000356
7.2%
1999314
6.3%
1998297
6.0%

Vehicle_Make_Description
Categorical

HIGH CARDINALITY

Distinct1166
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
FORD F150
 
165
CHEV PICKUP1500
 
130
FORD RANGER
 
120
CHEV S10 PICKUP
 
105
CHEV SILVER1500
 
91
Other values (1161)
4353 

Length

Max length18
Median length17
Mean length16.99979855
Min length16

Characters and Unicode

Total characters84387
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique574 ?
Unique (%)11.6%

Sample

1st rowDODG CARAVAN SE
2nd rowBUIK REG LS-LSE
3rd rowFORD TRUCK
4th rowTYTA TUNDRA SR5
5th rowCHEV CAMARO RS

Common Values

ValueCountFrequency (%)
FORD F150 165
 
3.3%
CHEV PICKUP1500 130
 
2.6%
FORD RANGER 120
 
2.4%
CHEV S10 PICKUP 105
 
2.1%
CHEV SILVER1500 91
 
1.8%
TYTA CAMRY 78
 
1.6%
HOND ACCORD EX 75
 
1.5%
DODG RAM PU1500 75
 
1.5%
FORD EXPLORER 71
 
1.4%
HOND ACCORD LX 63
 
1.3%
Other values (1156)3991
80.4%

Length

2021-09-12T14:16:17.457376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford1171
 
9.5%
chev1044
 
8.4%
tyta366
 
3.0%
dodg317
 
2.6%
hond266
 
2.2%
f150235
 
1.9%
pickup215
 
1.7%
nssn210
 
1.7%
jeep196
 
1.6%
gmc190
 
1.5%
Other values (1042)8153
65.9%

Most occurring characters

ValueCountFrequency (%)
26120
31.0%
R5044
 
6.0%
E4577
 
5.4%
C3725
 
4.4%
A3692
 
4.4%
O3465
 
4.1%
D3240
 
3.8%
S3189
 
3.8%
T2765
 
3.3%
L2595
 
3.1%
Other values (33)25975
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter53159
63.0%
Space Separator26120
31.0%
Decimal Number4456
 
5.3%
Dash Punctuation597
 
0.7%
Other Punctuation53
 
0.1%
Open Punctuation1
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R5044
 
9.5%
E4577
 
8.6%
C3725
 
7.0%
A3692
 
6.9%
O3465
 
6.5%
D3240
 
6.1%
S3189
 
6.0%
T2765
 
5.2%
L2595
 
4.9%
N2373
 
4.5%
Other values (16)18494
34.8%
Decimal Number
ValueCountFrequency (%)
01840
41.3%
1997
22.4%
5975
21.9%
2264
 
5.9%
3147
 
3.3%
4100
 
2.2%
859
 
1.3%
628
 
0.6%
725
 
0.6%
921
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.33
62.3%
&16
30.2%
\4
 
7.5%
Space Separator
ValueCountFrequency (%)
26120
100.0%
Dash Punctuation
ValueCountFrequency (%)
-597
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin53159
63.0%
Common31228
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R5044
 
9.5%
E4577
 
8.6%
C3725
 
7.0%
A3692
 
6.9%
O3465
 
6.5%
D3240
 
6.1%
S3189
 
6.0%
T2765
 
5.2%
L2595
 
4.9%
N2373
 
4.5%
Other values (16)18494
34.8%
Common
ValueCountFrequency (%)
26120
83.6%
01840
 
5.9%
1997
 
3.2%
5975
 
3.1%
-597
 
1.9%
2264
 
0.8%
3147
 
0.5%
4100
 
0.3%
859
 
0.2%
.33
 
0.1%
Other values (7)96
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII84387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26120
31.0%
R5044
 
6.0%
E4577
 
5.4%
C3725
 
4.4%
A3692
 
4.4%
O3465
 
4.1%
D3240
 
3.8%
S3189
 
3.8%
T2765
 
3.3%
L2595
 
3.1%
Other values (33)25975
30.8%

Vehicle_Performance
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Standard
4725 
Intermediate
 
134
High
 
72
Sports Premium
 
22
Sports
 
11

Length

Max length14
Median length8
Mean length8.072119259
Min length4

Characters and Unicode

Total characters40070
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowSports

Common Values

ValueCountFrequency (%)
Standard4725
95.2%
Intermediate134
 
2.7%
High72
 
1.5%
Sports Premium22
 
0.4%
Sports11
 
0.2%

Length

2021-09-12T14:16:17.584502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:17.625425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
standard4725
94.8%
intermediate134
 
2.7%
high72
 
1.4%
sports33
 
0.7%
premium22
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a9584
23.9%
d9584
23.9%
t5026
12.5%
r4914
12.3%
n4859
12.1%
S4758
11.9%
e424
 
1.1%
i228
 
0.6%
m178
 
0.4%
I134
 
0.3%
Other values (9)381
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35062
87.5%
Uppercase Letter4986
 
12.4%
Space Separator22
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a9584
27.3%
d9584
27.3%
t5026
14.3%
r4914
14.0%
n4859
13.9%
e424
 
1.2%
i228
 
0.7%
m178
 
0.5%
g72
 
0.2%
h72
 
0.2%
Other values (4)121
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S4758
95.4%
I134
 
2.7%
H72
 
1.4%
P22
 
0.4%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin40048
99.9%
Common22
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a9584
23.9%
d9584
23.9%
t5026
12.5%
r4914
12.3%
n4859
12.1%
S4758
11.9%
e424
 
1.1%
i228
 
0.6%
m178
 
0.4%
I134
 
0.3%
Other values (8)359
 
0.9%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII40070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a9584
23.9%
d9584
23.9%
t5026
12.5%
r4914
12.3%
n4859
12.1%
S4758
11.9%
e424
 
1.1%
i228
 
0.6%
m178
 
0.4%
I134
 
0.3%
Other values (9)381
 
1.0%

Vehicle_New_Cost_Amount
Real number (ℝ)

Distinct32
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.0012087
Minimum-1
Maximum45000
Zeros7
Zeros (%)0.1%
Negative4883
Negative (%)98.4%
Memory size38.9 KiB
2021-09-12T14:16:17.682214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum45000
Range45001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2418.717194
Coefficient of variation (CV)9.196601055
Kurtosis118.7991215
Mean263.0012087
Median Absolute Deviation (MAD)0
Skewness10.39626324
Sum1305538
Variance5850192.862
MonotonicityNot monotonic
2021-09-12T14:16:17.744298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
-14883
98.4%
07
 
0.1%
250006
 
0.1%
150006
 
0.1%
300005
 
0.1%
100005
 
0.1%
200005
 
0.1%
160005
 
0.1%
120004
 
0.1%
80004
 
0.1%
Other values (22)34
 
0.7%
ValueCountFrequency (%)
-14883
98.4%
07
 
0.1%
21
 
< 0.1%
191
 
< 0.1%
20001
 
< 0.1%
30002
 
< 0.1%
35001
 
< 0.1%
40001
 
< 0.1%
44001
 
< 0.1%
60001
 
< 0.1%
ValueCountFrequency (%)
450001
 
< 0.1%
400001
 
< 0.1%
320001
 
< 0.1%
300005
0.1%
285001
 
< 0.1%
280002
 
< 0.1%
260002
 
< 0.1%
250006
0.1%
240004
0.1%
230001
 
< 0.1%

Vehicle_Symbol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.2520145
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:17.808721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median11
Q314
95-th percentile18
Maximum26
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.046817738
Coefficient of variation (CV)0.3596527303
Kurtosis-0.02118968875
Mean11.2520145
Median Absolute Deviation (MAD)3
Skewness0.229020055
Sum55855
Variance16.3767338
MonotonicityNot monotonic
2021-09-12T14:16:17.871365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11570
11.5%
10561
11.3%
8535
10.8%
12483
9.7%
14447
9.0%
13405
8.2%
7346
7.0%
15312
 
6.3%
6251
 
5.1%
16243
 
4.9%
Other values (15)811
16.3%
ValueCountFrequency (%)
15
 
0.1%
229
 
0.6%
343
 
0.9%
4122
 
2.5%
5171
 
3.4%
6251
5.1%
7346
7.0%
8535
10.8%
10561
11.3%
11570
11.5%
ValueCountFrequency (%)
265
 
0.1%
253
 
0.1%
245
 
0.1%
2311
 
0.2%
2234
 
0.7%
2135
 
0.7%
2048
 
1.0%
1961
1.2%
18102
2.1%
17137
2.8%

Vehicle_Number_Of_Drivers_Assigned
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
3464 
99
1399 
2
 
94
3
 
6
4
 
1

Length

Max length2
Median length1
Mean length1.28182917
Min length1

Characters and Unicode

Total characters6363
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row99
4th row99
5th row1

Common Values

ValueCountFrequency (%)
13464
69.8%
991399
28.2%
294
 
1.9%
36
 
0.1%
41
 
< 0.1%

Length

2021-09-12T14:16:17.999912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:18.041928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
13464
69.8%
991399
28.2%
294
 
1.9%
36
 
0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
13464
54.4%
92798
44.0%
294
 
1.5%
36
 
0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6363
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13464
54.4%
92798
44.0%
294
 
1.5%
36
 
0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common6363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13464
54.4%
92798
44.0%
294
 
1.5%
36
 
0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13464
54.4%
92798
44.0%
294
 
1.5%
36
 
0.1%
41
 
< 0.1%

Vehicle_Usage
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Pleasure
2659 
Work
1824 
Farm
459 
Business
 
22

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters39712
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPleasure
2nd rowPleasure
3rd rowFarm
4th rowPleasure
5th rowPleasure

Common Values

ValueCountFrequency (%)
Pleasure2659
53.6%
Work 1824
36.7%
Farm 459
 
9.2%
Business22
 
0.4%

Length

2021-09-12T14:16:18.158793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:18.197731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
pleasure2659
53.6%
work1824
36.7%
farm459
 
9.2%
business22
 
0.4%

Most occurring characters

ValueCountFrequency (%)
9132
23.0%
e5340
13.4%
r4942
12.4%
a3118
 
7.9%
s2725
 
6.9%
u2681
 
6.8%
P2659
 
6.7%
l2659
 
6.7%
W1824
 
4.6%
o1824
 
4.6%
Other values (6)2808
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25616
64.5%
Space Separator9132
 
23.0%
Uppercase Letter4964
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5340
20.8%
r4942
19.3%
a3118
12.2%
s2725
10.6%
u2681
10.5%
l2659
10.4%
o1824
 
7.1%
k1824
 
7.1%
m459
 
1.8%
i22
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P2659
53.6%
W1824
36.7%
F459
 
9.2%
B22
 
0.4%
Space Separator
ValueCountFrequency (%)
9132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30580
77.0%
Common9132
 
23.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5340
17.5%
r4942
16.2%
a3118
10.2%
s2725
8.9%
u2681
8.8%
P2659
8.7%
l2659
8.7%
W1824
 
6.0%
o1824
 
6.0%
k1824
 
6.0%
Other values (5)984
 
3.2%
Common
ValueCountFrequency (%)
9132
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9132
23.0%
e5340
13.4%
r4942
12.4%
a3118
 
7.9%
s2725
 
6.9%
u2681
 
6.8%
P2659
 
6.7%
l2659
 
6.7%
W1824
 
4.6%
o1824
 
4.6%
Other values (6)2808
 
7.1%

Vehicle_Miles_To_Work
Real number (ℝ)

HIGH CORRELATION

Distinct44
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.253021757
Minimum-1
Maximum70
Zeros9
Zeros (%)0.2%
Negative3107
Negative (%)62.6%
Memory size38.9 KiB
2021-09-12T14:16:18.252325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q36
95-th percentile15
Maximum70
Range71
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.516818468
Coefficient of variation (CV)2.310718781
Kurtosis12.38461536
Mean3.253021757
Median Absolute Deviation (MAD)0
Skewness2.878665537
Sum16148
Variance56.50255988
MonotonicityNot monotonic
2021-09-12T14:16:18.323261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
-13107
62.6%
10362
 
7.3%
5224
 
4.5%
12166
 
3.3%
8131
 
2.6%
6106
 
2.1%
3105
 
2.1%
14104
 
2.1%
488
 
1.8%
274
 
1.5%
Other values (34)497
 
10.0%
ValueCountFrequency (%)
-13107
62.6%
09
 
0.2%
153
 
1.1%
274
 
1.5%
3105
 
2.1%
488
 
1.8%
5224
 
4.5%
6106
 
2.1%
774
 
1.5%
8131
 
2.6%
ValueCountFrequency (%)
702
 
< 0.1%
631
 
< 0.1%
601
 
< 0.1%
551
 
< 0.1%
531
 
< 0.1%
5015
0.3%
481
 
< 0.1%
456
 
0.1%
421
 
< 0.1%
4010
0.2%

Vehicle_Days_Per_Week_Driven
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
5
4963 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
54963
> 99.9%
11
 
< 0.1%

Length

2021-09-12T14:16:18.447620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:18.484196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
54963
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
54963
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
54963
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
54963
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54963
> 99.9%
11
 
< 0.1%

Vehicle_Annual_Miles
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Unknown
4962 
0
 
2

Length

Max length7
Median length7
Mean length6.997582595
Min length1

Characters and Unicode

Total characters34736
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown4962
> 99.9%
02
 
< 0.1%

Length

2021-09-12T14:16:18.581299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:18.620737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown4962
> 99.9%
02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n14886
42.9%
U4962
 
14.3%
k4962
 
14.3%
o4962
 
14.3%
w4962
 
14.3%
02
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29772
85.7%
Uppercase Letter4962
 
14.3%
Decimal Number2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n14886
50.0%
k4962
 
16.7%
o4962
 
16.7%
w4962
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
U4962
100.0%
Decimal Number
ValueCountFrequency (%)
02
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin34734
> 99.9%
Common2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n14886
42.9%
U4962
 
14.3%
k4962
 
14.3%
o4962
 
14.3%
w4962
 
14.3%
Common
ValueCountFrequency (%)
02
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII34736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n14886
42.9%
U4962
 
14.3%
k4962
 
14.3%
o4962
 
14.3%
w4962
 
14.3%
02
 
< 0.1%

Vehicle_Anti_Theft_Device
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Not Applicable
2902 
Passive Disabling-Vehicle Recovery
1563 
Alarm Only
390 
Active Disabling
 
109

Length

Max length34
Median length14
Mean length20.02699436
Min length10

Characters and Unicode

Total characters99414
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable2902
58.5%
Passive Disabling-Vehicle Recovery1563
31.5%
Alarm Only390
 
7.9%
Active Disabling109
 
2.2%

Length

2021-09-12T14:16:18.733520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:18.774691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
not2902
25.3%
applicable2902
25.3%
passive1563
13.6%
disabling-vehicle1563
13.6%
recovery1563
13.6%
alarm390
 
3.4%
only390
 
3.4%
active109
 
0.9%
disabling109
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e10826
 
10.9%
l9819
 
9.9%
i9481
 
9.5%
6527
 
6.6%
a6527
 
6.6%
c6137
 
6.2%
p5804
 
5.8%
s4798
 
4.8%
b4574
 
4.6%
o4465
 
4.5%
Other values (16)30456
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter78270
78.7%
Uppercase Letter13054
 
13.1%
Space Separator6527
 
6.6%
Dash Punctuation1563
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10826
13.8%
l9819
12.5%
i9481
12.1%
a6527
8.3%
c6137
7.8%
p5804
7.4%
s4798
 
6.1%
b4574
 
5.8%
o4465
 
5.7%
v3235
 
4.1%
Other values (7)12604
16.1%
Uppercase Letter
ValueCountFrequency (%)
A3401
26.1%
N2902
22.2%
D1672
12.8%
P1563
12.0%
V1563
12.0%
R1563
12.0%
O390
 
3.0%
Space Separator
ValueCountFrequency (%)
6527
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1563
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin91324
91.9%
Common8090
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10826
11.9%
l9819
 
10.8%
i9481
 
10.4%
a6527
 
7.1%
c6137
 
6.7%
p5804
 
6.4%
s4798
 
5.3%
b4574
 
5.0%
o4465
 
4.9%
A3401
 
3.7%
Other values (14)25492
27.9%
Common
ValueCountFrequency (%)
6527
80.7%
-1563
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII99414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10826
 
10.9%
l9819
 
9.9%
i9481
 
9.5%
6527
 
6.6%
a6527
 
6.6%
c6137
 
6.2%
p5804
 
5.8%
s4798
 
4.8%
b4574
 
4.6%
o4465
 
4.5%
Other values (16)30456
30.6%

Vehicle_Passive_Restraint
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Y
3648 
N
1314 
Unknown
 
2

Length

Max length7
Median length1
Mean length1.002417405
Min length1

Characters and Unicode

Total characters4976
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowN
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
Y3648
73.5%
N1314
 
26.5%
Unknown2
 
< 0.1%

Length

2021-09-12T14:16:18.896833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:18.937090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
y3648
73.5%
n1314
 
26.5%
unknown2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Y3648
73.3%
N1314
 
26.4%
n6
 
0.1%
U2
 
< 0.1%
k2
 
< 0.1%
o2
 
< 0.1%
w2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4964
99.8%
Lowercase Letter12
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n6
50.0%
k2
 
16.7%
o2
 
16.7%
w2
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
Y3648
73.5%
N1314
 
26.5%
U2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin4976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y3648
73.3%
N1314
 
26.4%
n6
 
0.1%
U2
 
< 0.1%
k2
 
< 0.1%
o2
 
< 0.1%
w2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y3648
73.3%
N1314
 
26.4%
n6
 
0.1%
U2
 
< 0.1%
k2
 
< 0.1%
o2
 
< 0.1%
w2
 
< 0.1%

Vehicle_Age_In_Years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.067284448
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:18.976235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.577883233
Coefficient of variation (CV)0.3647629089
Kurtosis-0.3812779045
Mean7.067284448
Median Absolute Deviation (MAD)0
Skewness-1.023117
Sum35082
Variance6.645481965
MonotonicityNot monotonic
2021-09-12T14:16:19.029354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
92682
54.0%
7360
 
7.3%
8305
 
6.1%
6300
 
6.0%
5294
 
5.9%
4284
 
5.7%
3282
 
5.7%
2275
 
5.5%
1182
 
3.7%
ValueCountFrequency (%)
1182
 
3.7%
2275
 
5.5%
3282
 
5.7%
4284
 
5.7%
5294
 
5.9%
6300
 
6.0%
7360
 
7.3%
8305
 
6.1%
92682
54.0%
ValueCountFrequency (%)
92682
54.0%
8305
 
6.1%
7360
 
7.3%
6300
 
6.0%
5294
 
5.9%
4284
 
5.7%
3282
 
5.7%
2275
 
5.5%
1182
 
3.7%

Vehicle_Med_Pay_Limit
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2945.563457
Minimum-1
Maximum50000
Zeros0
Zeros (%)0.0%
Negative1223
Negative (%)24.6%
Memory size38.9 KiB
2021-09-12T14:16:19.084134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11000
median1000
Q35000
95-th percentile10000
Maximum50000
Range50001
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation6826.446262
Coefficient of variation (CV)2.317534951
Kurtosis34.89778382
Mean2945.563457
Median Absolute Deviation (MAD)1001
Skewness5.652149498
Sum14621777
Variance46600368.57
MonotonicityNot monotonic
2021-09-12T14:16:19.136613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10002209
44.5%
-11223
24.6%
50001126
22.7%
2000147
 
3.0%
10000134
 
2.7%
5000081
 
1.6%
2500044
 
0.9%
ValueCountFrequency (%)
-11223
24.6%
10002209
44.5%
2000147
 
3.0%
50001126
22.7%
10000134
 
2.7%
2500044
 
0.9%
5000081
 
1.6%
ValueCountFrequency (%)
5000081
 
1.6%
2500044
 
0.9%
10000134
 
2.7%
50001126
22.7%
2000147
 
3.0%
10002209
44.5%
-11223
24.6%

Vehicle_Bodily_Injury_Limit
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing194
Missing (%)3.9%
Memory size38.9 KiB
25-50
1706 
100-300
1590 
50-100
1210 
250-500
239 
300-500
 
11
Other values (2)
 
14

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters33390
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25-50
2nd row25-50
3rd row250-500
4th row100-300
5th row25-50

Common Values

ValueCountFrequency (%)
25-50 1706
34.4%
100-3001590
32.0%
50-100 1210
24.4%
250-500239
 
4.8%
300-50011
 
0.2%
500-50010
 
0.2%
100-5004
 
0.1%
(Missing)194
 
3.9%

Length

2021-09-12T14:16:19.261210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:19.301928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
25-501706
35.8%
100-3001590
33.3%
50-1001210
25.4%
250-500239
 
5.0%
300-50011
 
0.2%
500-50010
 
0.2%
100-5004
 
0.1%

Most occurring characters

ValueCountFrequency (%)
012513
37.5%
55135
15.4%
-4770
 
14.3%
4622
 
13.8%
12804
 
8.4%
21945
 
5.8%
31601
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23998
71.9%
Dash Punctuation4770
 
14.3%
Space Separator4622
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012513
52.1%
55135
21.4%
12804
 
11.7%
21945
 
8.1%
31601
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
-4770
100.0%
Space Separator
ValueCountFrequency (%)
4622
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common33390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012513
37.5%
55135
15.4%
-4770
 
14.3%
4622
 
13.8%
12804
 
8.4%
21945
 
5.8%
31601
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII33390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
012513
37.5%
55135
15.4%
-4770
 
14.3%
4622
 
13.8%
12804
 
8.4%
21945
 
5.8%
31601
 
4.8%

Vehicle_Physical_Damage_Limit
Real number (ℝ)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48251.37107
Minimum-1
Maximum500000
Zeros0
Zeros (%)0.0%
Negative194
Negative (%)3.9%
Memory size38.9 KiB
2021-09-12T14:16:19.356286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile25000
Q125000
median50000
Q350000
95-th percentile100000
Maximum500000
Range500001
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation37679.7618
Coefficient of variation (CV)0.7809055155
Kurtosis45.98639559
Mean48251.37107
Median Absolute Deviation (MAD)25000
Skewness4.922716924
Sum239519806
Variance1419764449
MonotonicityNot monotonic
2021-09-12T14:16:19.415424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
500001967
39.6%
250001718
34.6%
100000723
 
14.6%
35000312
 
6.3%
-1194
 
3.9%
25000040
 
0.8%
50000010
 
0.2%
ValueCountFrequency (%)
-1194
 
3.9%
250001718
34.6%
35000312
 
6.3%
500001967
39.6%
100000723
 
14.6%
25000040
 
0.8%
50000010
 
0.2%
ValueCountFrequency (%)
50000010
 
0.2%
25000040
 
0.8%
100000723
 
14.6%
500001967
39.6%
35000312
 
6.3%
250001718
34.6%
-1194
 
3.9%

Vehicle_Comprehensive_Coverage_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4776 
True
 
188
ValueCountFrequency (%)
False4776
96.2%
True188
 
3.8%
2021-09-12T14:16:19.457403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Vehicle_Comprehensive_Coverage_Limit
Real number (ℝ)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6158.385173
Minimum-1
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative4776
Negative (%)96.2%
Memory size38.9 KiB
2021-09-12T14:16:19.494131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum1000000
Range1000001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42766.89152
Coefficient of variation (CV)6.944497675
Kurtosis138.8361865
Mean6158.385173
Median Absolute Deviation (MAD)0
Skewness10.51994148
Sum30570224
Variance1829007010
MonotonicityNot monotonic
2021-09-12T14:16:19.548846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-14776
96.2%
7500081
 
1.6%
10000061
 
1.2%
30000024
 
0.5%
50000020
 
0.4%
2000001
 
< 0.1%
10000001
 
< 0.1%
ValueCountFrequency (%)
-14776
96.2%
7500081
 
1.6%
10000061
 
1.2%
2000001
 
< 0.1%
30000024
 
0.5%
50000020
 
0.4%
10000001
 
< 0.1%
ValueCountFrequency (%)
10000001
 
< 0.1%
50000020
 
0.4%
30000024
 
0.5%
2000001
 
< 0.1%
10000061
 
1.2%
7500081
 
1.6%
-14776
96.2%

Vehicle_Collision_Coverage_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
3005 
False
1959 
ValueCountFrequency (%)
True3005
60.5%
False1959
39.5%
2021-09-12T14:16:19.589147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Vehicle_Collision_Coverage_Deductible
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.4582998
Minimum-1
Maximum3000
Zeros0
Zeros (%)0.0%
Negative1959
Negative (%)39.5%
Memory size38.9 KiB
2021-09-12T14:16:19.624204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median250
Q3500
95-th percentile500
Maximum3000
Range3001
Interquartile range (IQR)501

Descriptive statistics

Standard deviation276.6693375
Coefficient of variation (CV)0.9395874992
Kurtosis1.536179161
Mean294.4582998
Median Absolute Deviation (MAD)250
Skewness0.6222835143
Sum1461691
Variance76545.9223
MonotonicityNot monotonic
2021-09-12T14:16:19.675226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5002235
45.0%
-11959
39.5%
250528
 
10.6%
1000205
 
4.1%
10027
 
0.5%
2007
 
0.1%
501
 
< 0.1%
30001
 
< 0.1%
20001
 
< 0.1%
ValueCountFrequency (%)
-11959
39.5%
501
 
< 0.1%
10027
 
0.5%
2007
 
0.1%
250528
 
10.6%
5002235
45.0%
1000205
 
4.1%
20001
 
< 0.1%
30001
 
< 0.1%
ValueCountFrequency (%)
30001
 
< 0.1%
20001
 
< 0.1%
1000205
 
4.1%
5002235
45.0%
250528
 
10.6%
2007
 
0.1%
10027
 
0.5%
501
 
< 0.1%
-11959
39.5%

Driver_Total
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
4368 
2
595 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
14368
88.0%
2595
 
12.0%
31
 
< 0.1%

Length

2021-09-12T14:16:20.161716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:20.205008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
14368
88.0%
2595
 
12.0%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
14368
88.0%
2595
 
12.0%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14368
88.0%
2595
 
12.0%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14368
88.0%
2595
 
12.0%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14368
88.0%
2595
 
12.0%
31
 
< 0.1%

Driver_Total_Male
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
2626 
0
2311 
2
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12626
52.9%
02311
46.6%
227
 
0.5%

Length

2021-09-12T14:16:20.317689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:20.362228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
12626
52.9%
02311
46.6%
227
 
0.5%

Most occurring characters

ValueCountFrequency (%)
12626
52.9%
02311
46.6%
227
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12626
52.9%
02311
46.6%
227
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12626
52.9%
02311
46.6%
227
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12626
52.9%
02311
46.6%
227
 
0.5%

Driver_Total_Female
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
2799 
0
2124 
2
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
12799
56.4%
02124
42.8%
241
 
0.8%

Length

2021-09-12T14:16:20.483383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:20.523465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
12799
56.4%
02124
42.8%
241
 
0.8%

Most occurring characters

ValueCountFrequency (%)
12799
56.4%
02124
42.8%
241
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12799
56.4%
02124
42.8%
241
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12799
56.4%
02124
42.8%
241
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12799
56.4%
02124
42.8%
241
 
0.8%

Driver_Total_Single
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3796 
1
1148 
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03796
76.5%
11148
 
23.1%
220
 
0.4%

Length

2021-09-12T14:16:20.653244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:20.705344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03796
76.5%
11148
 
23.1%
220
 
0.4%

Most occurring characters

ValueCountFrequency (%)
03796
76.5%
11148
 
23.1%
220
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03796
76.5%
11148
 
23.1%
220
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03796
76.5%
11148
 
23.1%
220
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03796
76.5%
11148
 
23.1%
220
 
0.4%

Driver_Total_Married
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
2554 
0
1940 
2
470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
12554
51.5%
01940
39.1%
2470
 
9.5%

Length

2021-09-12T14:16:20.828512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:20.876500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
12554
51.5%
01940
39.1%
2470
 
9.5%

Most occurring characters

ValueCountFrequency (%)
12554
51.5%
01940
39.1%
2470
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12554
51.5%
01940
39.1%
2470
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12554
51.5%
01940
39.1%
2470
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12554
51.5%
01940
39.1%
2470
 
9.5%

Driver_Total_Related_To_Insured_Self
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
3801 
0
1158 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13801
76.6%
01158
 
23.3%
25
 
0.1%

Length

2021-09-12T14:16:21.015529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:21.061343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
13801
76.6%
01158
 
23.3%
25
 
0.1%

Most occurring characters

ValueCountFrequency (%)
13801
76.6%
01158
 
23.3%
25
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13801
76.6%
01158
 
23.3%
25
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13801
76.6%
01158
 
23.3%
25
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13801
76.6%
01158
 
23.3%
25
 
0.1%

Driver_Total_Related_To_Insured_Spouse
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3693 
1
1270 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03693
74.4%
11270
 
25.6%
21
 
< 0.1%

Length

2021-09-12T14:16:21.184409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:21.240048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03693
74.4%
11270
 
25.6%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
03693
74.4%
11270
 
25.6%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03693
74.4%
11270
 
25.6%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03693
74.4%
11270
 
25.6%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03693
74.4%
11270
 
25.6%
21
 
< 0.1%

Driver_Total_Related_To_Insured_Child
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4602 
1
 
359
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04602
92.7%
1359
 
7.2%
23
 
0.1%

Length

2021-09-12T14:16:21.380338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:21.423982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04602
92.7%
1359
 
7.2%
23
 
0.1%

Most occurring characters

ValueCountFrequency (%)
04602
92.7%
1359
 
7.2%
23
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04602
92.7%
1359
 
7.2%
23
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04602
92.7%
1359
 
7.2%
23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04602
92.7%
1359
 
7.2%
23
 
0.1%

Driver_Total_Licensed_In_State
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
4359 
2
592 
0
 
12
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
14359
87.8%
2592
 
11.9%
012
 
0.2%
31
 
< 0.1%

Length

2021-09-12T14:16:21.532948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:21.574202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
14359
87.8%
2592
 
11.9%
012
 
0.2%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
14359
87.8%
2592
 
11.9%
012
 
0.2%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14359
87.8%
2592
 
11.9%
012
 
0.2%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14359
87.8%
2592
 
11.9%
012
 
0.2%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14359
87.8%
2592
 
11.9%
012
 
0.2%
31
 
< 0.1%

Driver_Minimum_Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.08843674
Minimum16
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:21.634072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile20
Q135
median46
Q357
95-th percentile73
Maximum93
Range77
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.8180783
Coefficient of variation (CV)0.3432114304
Kurtosis-0.4738025737
Mean46.08843674
Median Absolute Deviation (MAD)11
Skewness0.2218588281
Sum228783
Variance250.2116011
MonotonicityNot monotonic
2021-09-12T14:16:21.715835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41141
 
2.8%
39132
 
2.7%
51128
 
2.6%
49119
 
2.4%
48119
 
2.4%
45118
 
2.4%
47115
 
2.3%
46114
 
2.3%
35114
 
2.3%
44112
 
2.3%
Other values (67)3752
75.6%
ValueCountFrequency (%)
1638
0.8%
1745
0.9%
1854
1.1%
1969
1.4%
2050
1.0%
2156
1.1%
2245
0.9%
2350
1.0%
2454
1.1%
2569
1.4%
ValueCountFrequency (%)
931
 
< 0.1%
913
 
0.1%
902
 
< 0.1%
894
 
0.1%
884
 
0.1%
878
0.2%
8610
0.2%
8512
0.2%
8411
0.2%
8317
0.3%

Driver_Maximum_Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.02921031
Minimum16
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:21.800942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile21
Q136
median47
Q358
95-th percentile74
Maximum93
Range77
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.59354762
Coefficient of variation (CV)0.3315715386
Kurtosis-0.4507331482
Mean47.02921031
Median Absolute Deviation (MAD)11
Skewness0.2057878855
Sum233453
Variance243.1587275
MonotonicityNot monotonic
2021-09-12T14:16:21.881574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41144
 
2.9%
39139
 
2.8%
49132
 
2.7%
51132
 
2.7%
48126
 
2.5%
45123
 
2.5%
52123
 
2.5%
43119
 
2.4%
42115
 
2.3%
35114
 
2.3%
Other values (67)3697
74.5%
ValueCountFrequency (%)
1627
0.5%
1732
0.6%
1840
0.8%
1959
1.2%
2042
0.8%
2150
1.0%
2242
0.8%
2345
0.9%
2451
1.0%
2559
1.2%
ValueCountFrequency (%)
931
 
< 0.1%
914
 
0.1%
902
 
< 0.1%
894
 
0.1%
884
 
0.1%
878
0.2%
8610
0.2%
8512
0.2%
8411
0.2%
8317
0.3%

Driver_Total_Teenager_Age_15_19
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4758 
1
 
204
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04758
95.9%
1204
 
4.1%
22
 
< 0.1%

Length

2021-09-12T14:16:22.015463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:22.053276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04758
95.9%
1204
 
4.1%
22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
04758
95.9%
1204
 
4.1%
22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04758
95.9%
1204
 
4.1%
22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04758
95.9%
1204
 
4.1%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04758
95.9%
1204
 
4.1%
22
 
< 0.1%

Driver_Total_College_Ages_20_23
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4761 
1
 
199
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04761
95.9%
1199
 
4.0%
24
 
0.1%

Length

2021-09-12T14:16:22.155392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:22.192952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04761
95.9%
1199
 
4.0%
24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
04761
95.9%
1199
 
4.0%
24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04761
95.9%
1199
 
4.0%
24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04761
95.9%
1199
 
4.0%
24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04761
95.9%
1199
 
4.0%
24
 
0.1%

Driver_Total_Young_Adult_Ages_24_29
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4539 
1
 
390
2
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04539
91.4%
1390
 
7.9%
235
 
0.7%

Length

2021-09-12T14:16:22.302931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:22.340882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04539
91.4%
1390
 
7.9%
235
 
0.7%

Most occurring characters

ValueCountFrequency (%)
04539
91.4%
1390
 
7.9%
235
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04539
91.4%
1390
 
7.9%
235
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04539
91.4%
1390
 
7.9%
235
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04539
91.4%
1390
 
7.9%
235
 
0.7%

Driver_Total_Low_Middle_Adult_Ages_30_39
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3959 
1
944 
2
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
03959
79.8%
1944
 
19.0%
261
 
1.2%

Length

2021-09-12T14:16:22.454680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:22.492543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03959
79.8%
1944
 
19.0%
261
 
1.2%

Most occurring characters

ValueCountFrequency (%)
03959
79.8%
1944
 
19.0%
261
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03959
79.8%
1944
 
19.0%
261
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03959
79.8%
1944
 
19.0%
261
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03959
79.8%
1944
 
19.0%
261
 
1.2%

Driver_Total_Middle_Adult_Ages_40_49
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3720 
1
1175 
2
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03720
74.9%
11175
 
23.7%
269
 
1.4%

Length

2021-09-12T14:16:22.607004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:22.645195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03720
74.9%
11175
 
23.7%
269
 
1.4%

Most occurring characters

ValueCountFrequency (%)
03720
74.9%
11175
 
23.7%
269
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03720
74.9%
11175
 
23.7%
269
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03720
74.9%
11175
 
23.7%
269
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03720
74.9%
11175
 
23.7%
269
 
1.4%

Driver_Total_Adult_Ages_50_64
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3514 
1
1339 
2
 
111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03514
70.8%
11339
 
27.0%
2111
 
2.2%

Length

2021-09-12T14:16:22.753533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:22.791790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03514
70.8%
11339
 
27.0%
2111
 
2.2%

Most occurring characters

ValueCountFrequency (%)
03514
70.8%
11339
 
27.0%
2111
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03514
70.8%
11339
 
27.0%
2111
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03514
70.8%
11339
 
27.0%
2111
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03514
70.8%
11339
 
27.0%
2111
 
2.2%

Driver_Total_Senior_Ages_65_69
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4666 
1
 
285
2
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04666
94.0%
1285
 
5.7%
213
 
0.3%

Length

2021-09-12T14:16:22.912024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:22.950708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04666
94.0%
1285
 
5.7%
213
 
0.3%

Most occurring characters

ValueCountFrequency (%)
04666
94.0%
1285
 
5.7%
213
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04666
94.0%
1285
 
5.7%
213
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04666
94.0%
1285
 
5.7%
213
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04666
94.0%
1285
 
5.7%
213
 
0.3%

Driver_Total_Upper_Senior_Ages_70_plus
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4550 
1
 
393
2
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04550
91.7%
1393
 
7.9%
221
 
0.4%

Length

2021-09-12T14:16:23.056778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:23.096038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04550
91.7%
1393
 
7.9%
221
 
0.4%

Most occurring characters

ValueCountFrequency (%)
04550
91.7%
1393
 
7.9%
221
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04550
91.7%
1393
 
7.9%
221
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04550
91.7%
1393
 
7.9%
221
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04550
91.7%
1393
 
7.9%
221
 
0.4%

Vehicle_Youthful_Driver_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4478 
True
486 
ValueCountFrequency (%)
False4478
90.2%
True486
 
9.8%
2021-09-12T14:16:23.143696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Vehicle_Youthful_Driver_Training_Code
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Not Applicable
4478 
With or Without Driver Training
 
233
Without Driver Training
 
214
With Driver Training
 
39

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters153884
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable 4478
90.2%
With or Without Driver Training233
 
4.7%
Without Driver Training 214
 
4.3%
With Driver Training 39
 
0.8%

Length

2021-09-12T14:16:23.276513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:23.317738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
not4478
41.2%
applicable4478
41.2%
training486
 
4.5%
driver486
 
4.5%
without447
 
4.1%
with272
 
2.5%
or233
 
2.1%

Most occurring characters

ValueCountFrequency (%)
84183
54.7%
p8956
 
5.8%
l8956
 
5.8%
i6655
 
4.3%
t5644
 
3.7%
o5158
 
3.4%
a4964
 
3.2%
e4964
 
3.2%
N4478
 
2.9%
A4478
 
2.9%
Other values (11)15448
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator84183
54.7%
Lowercase Letter59054
38.4%
Uppercase Letter10647
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p8956
15.2%
l8956
15.2%
i6655
11.3%
t5644
9.6%
o5158
8.7%
a4964
8.4%
e4964
8.4%
c4478
7.6%
b4478
7.6%
r1691
 
2.9%
Other values (5)3110
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
N4478
42.1%
A4478
42.1%
W719
 
6.8%
D486
 
4.6%
T486
 
4.6%
Space Separator
ValueCountFrequency (%)
84183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common84183
54.7%
Latin69701
45.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
p8956
12.8%
l8956
12.8%
i6655
9.5%
t5644
8.1%
o5158
7.4%
a4964
7.1%
e4964
7.1%
N4478
6.4%
A4478
6.4%
c4478
6.4%
Other values (10)10970
15.7%
Common
ValueCountFrequency (%)
84183
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII153884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84183
54.7%
p8956
 
5.8%
l8956
 
5.8%
i6655
 
4.3%
t5644
 
3.7%
o5158
 
3.4%
a4964
 
3.2%
e4964
 
3.2%
N4478
 
2.9%
A4478
 
2.9%
Other values (11)15448
 
10.0%

Vehicle_Youthful_Good_Student_Code
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Not Eligible for Good Student Credit
4902 
Eligible for Good Student Credit
 
62

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters178704
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Eligible for Good Student Credit
2nd rowNot Eligible for Good Student Credit
3rd rowNot Eligible for Good Student Credit
4th rowNot Eligible for Good Student Credit
5th rowNot Eligible for Good Student Credit

Common Values

ValueCountFrequency (%)
Not Eligible for Good Student Credit4902
98.8%
Eligible for Good Student Credit 62
 
1.2%

Length

2021-09-12T14:16:23.431896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:23.476288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
for4964
16.7%
credit4964
16.7%
good4964
16.7%
student4964
16.7%
eligible4964
16.7%
not4902
16.5%

Most occurring characters

ValueCountFrequency (%)
25006
14.0%
o19794
11.1%
t19794
11.1%
i14892
 
8.3%
e14892
 
8.3%
d14892
 
8.3%
l9928
 
5.6%
r9928
 
5.6%
E4964
 
2.8%
g4964
 
2.8%
Other values (8)39650
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter128940
72.2%
Space Separator25006
 
14.0%
Uppercase Letter24758
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o19794
15.4%
t19794
15.4%
i14892
11.5%
e14892
11.5%
d14892
11.5%
l9928
7.7%
r9928
7.7%
g4964
 
3.8%
b4964
 
3.8%
f4964
 
3.8%
Other values (2)9928
7.7%
Uppercase Letter
ValueCountFrequency (%)
E4964
20.1%
G4964
20.1%
S4964
20.1%
C4964
20.1%
N4902
19.8%
Space Separator
ValueCountFrequency (%)
25006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin153698
86.0%
Common25006
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o19794
12.9%
t19794
12.9%
i14892
9.7%
e14892
9.7%
d14892
9.7%
l9928
 
6.5%
r9928
 
6.5%
E4964
 
3.2%
g4964
 
3.2%
b4964
 
3.2%
Other values (7)34686
22.6%
Common
ValueCountFrequency (%)
25006
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII178704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25006
14.0%
o19794
11.1%
t19794
11.1%
i14892
 
8.3%
e14892
 
8.3%
d14892
 
8.3%
l9928
 
5.6%
r9928
 
5.6%
E4964
 
2.8%
g4964
 
2.8%
Other values (8)39650
22.2%

Vehicle_Driver_Points
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4442 
1
 
441
2
 
72
3
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04442
89.5%
1441
 
8.9%
272
 
1.5%
39
 
0.2%

Length

2021-09-12T14:16:23.578128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:23.616162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04442
89.5%
1441
 
8.9%
272
 
1.5%
39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
04442
89.5%
1441
 
8.9%
272
 
1.5%
39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04442
89.5%
1441
 
8.9%
272
 
1.5%
39
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04442
89.5%
1441
 
8.9%
272
 
1.5%
39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04442
89.5%
1441
 
8.9%
272
 
1.5%
39
 
0.2%

Vehicle_Safe_Driver_Discount_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
2927 
False
2037 
ValueCountFrequency (%)
True2927
59.0%
False2037
41.0%
2021-09-12T14:16:23.644650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

EEA_Liability_Coverage_Only_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3602 
True
1362 
ValueCountFrequency (%)
False3602
72.6%
True1362
 
27.4%
2021-09-12T14:16:23.666894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

EEA_Multi_Auto_Policies_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
4384 
False
580 
ValueCountFrequency (%)
True4384
88.3%
False580
 
11.7%
2021-09-12T14:16:23.699086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

EEA_Policy_Zip_Code_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
428
628 
430
547 
424
497 
429
403 
433
341 
Other values (22)
2548 

Length

Max length7
Median length3
Mean length3.021756648
Min length3

Characters and Unicode

Total characters15000
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row426
2nd row428
3rd row429
4th row430
5th row424

Common Values

ValueCountFrequency (%)
428628
12.7%
430547
11.0%
424497
10.0%
429403
 
8.1%
433341
 
6.9%
425330
 
6.6%
441281
 
5.7%
438279
 
5.6%
439231
 
4.7%
427226
 
4.6%
Other values (17)1201
24.2%

Length

2021-09-12T14:16:23.819639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428628
12.7%
430547
11.0%
424497
10.0%
429403
 
8.1%
433341
 
6.9%
425330
 
6.6%
441281
 
5.7%
438279
 
5.6%
439231
 
4.7%
427226
 
4.6%
Other values (17)1201
24.2%

Most occurring characters

ValueCountFrequency (%)
45946
39.6%
22620
17.5%
32465
16.4%
8908
 
6.1%
0687
 
4.6%
9634
 
4.2%
1526
 
3.5%
5489
 
3.3%
6272
 
1.8%
7264
 
1.8%
Other values (5)189
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14811
98.7%
Lowercase Letter162
 
1.1%
Uppercase Letter27
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
45946
40.1%
22620
17.7%
32465
16.6%
8908
 
6.1%
0687
 
4.6%
9634
 
4.3%
1526
 
3.6%
5489
 
3.3%
6272
 
1.8%
7264
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
n81
50.0%
k27
 
16.7%
o27
 
16.7%
w27
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
U27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common14811
98.7%
Latin189
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
45946
40.1%
22620
17.7%
32465
16.6%
8908
 
6.1%
0687
 
4.6%
9634
 
4.3%
1526
 
3.6%
5489
 
3.3%
6272
 
1.8%
7264
 
1.8%
Latin
ValueCountFrequency (%)
n81
42.9%
U27
 
14.3%
k27
 
14.3%
o27
 
14.3%
w27
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45946
39.6%
22620
17.5%
32465
16.4%
8908
 
6.1%
0687
 
4.6%
9634
 
4.2%
1526
 
3.5%
5489
 
3.3%
6272
 
1.8%
7264
 
1.8%
Other values (5)189
 
1.3%

EEA_Policy_Tenure
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct285
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.686603546
Minimum0
Maximum46.9
Zeros459
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:23.888745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2
median3.5
Q38
95-th percentile19.47
Maximum46.9
Range46.9
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation6.450665849
Coefficient of variation (CV)1.134361803
Kurtosis5.210895051
Mean5.686603546
Median Absolute Deviation (MAD)2.6
Skewness2.035923485
Sum28228.3
Variance41.61108989
MonotonicityNot monotonic
2021-09-12T14:16:23.961489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0459
 
9.2%
0.5369
 
7.4%
1339
 
6.8%
1.5273
 
5.5%
2273
 
5.5%
2.5222
 
4.5%
3201
 
4.0%
3.5179
 
3.6%
4138
 
2.8%
5112
 
2.3%
Other values (275)2399
48.3%
ValueCountFrequency (%)
0459
9.2%
0.12
 
< 0.1%
0.22
 
< 0.1%
0.313
 
0.3%
0.43
 
0.1%
0.5369
7.4%
0.613
 
0.3%
0.74
 
0.1%
0.815
 
0.3%
0.94
 
0.1%
ValueCountFrequency (%)
46.91
< 0.1%
46.61
< 0.1%
45.21
< 0.1%
44.51
< 0.1%
441
< 0.1%
43.51
< 0.1%
41.11
< 0.1%
40.81
< 0.1%
39.21
< 0.1%
37.81
< 0.1%

EEA_Agency_Type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Hybrid
1386 
Preferred
1283 
Standard
1227 
Non-standard
1068 

Length

Max length12
Median length8
Mean length8.560636583
Min length6

Characters and Unicode

Total characters42495
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHybrid
2nd rowStandard
3rd rowNon-standard
4th rowHybrid
5th rowNon-standard

Common Values

ValueCountFrequency (%)
Hybrid1386
27.9%
Preferred1283
25.8%
Standard1227
24.7%
Non-standard1068
21.5%

Length

2021-09-12T14:16:24.099267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:24.143456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hybrid1386
27.9%
preferred1283
25.8%
standard1227
24.7%
non-standard1068
21.5%

Most occurring characters

ValueCountFrequency (%)
r7530
17.7%
d7259
17.1%
a4590
10.8%
e3849
9.1%
n3363
7.9%
t2295
 
5.4%
H1386
 
3.3%
y1386
 
3.3%
b1386
 
3.3%
i1386
 
3.3%
Other values (7)8065
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36463
85.8%
Uppercase Letter4964
 
11.7%
Dash Punctuation1068
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r7530
20.7%
d7259
19.9%
a4590
12.6%
e3849
10.6%
n3363
9.2%
t2295
 
6.3%
y1386
 
3.8%
b1386
 
3.8%
i1386
 
3.8%
f1283
 
3.5%
Other values (2)2136
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
H1386
27.9%
P1283
25.8%
S1227
24.7%
N1068
21.5%
Dash Punctuation
ValueCountFrequency (%)
-1068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin41427
97.5%
Common1068
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r7530
18.2%
d7259
17.5%
a4590
11.1%
e3849
9.3%
n3363
8.1%
t2295
 
5.5%
H1386
 
3.3%
y1386
 
3.3%
b1386
 
3.3%
i1386
 
3.3%
Other values (6)6997
16.9%
Common
ValueCountFrequency (%)
-1068
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r7530
17.7%
d7259
17.1%
a4590
10.8%
e3849
9.1%
n3363
7.9%
t2295
 
5.4%
H1386
 
3.3%
y1386
 
3.3%
b1386
 
3.3%
i1386
 
3.3%
Other values (7)8065
19.0%

EEA_Packaged_Policy_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
2641 
True
2323 
ValueCountFrequency (%)
False2641
53.2%
True2323
46.8%
2021-09-12T14:16:24.174792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

EEA_Full_Coverage_Indicator
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
3025 
False
1939 
ValueCountFrequency (%)
True3025
60.9%
False1939
39.1%
2021-09-12T14:16:24.196876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

EEA_Prior_Bodily_Injury_Limit
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing194
Missing (%)3.9%
Memory size38.9 KiB
20-50
1706 
100-200
1590 
40-100
1210 
100-400
239 
200-400
 
11
Other values (2)
 
14

Length

Max length7
Median length7
Mean length6.283857442
Min length5

Characters and Unicode

Total characters29974
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20-50
2nd row20-50
3rd row100-400
4th row100-200
5th row20-50

Common Values

ValueCountFrequency (%)
20-501706
34.4%
100-2001590
32.0%
40-100 1210
24.4%
100-400239
 
4.8%
200-40011
 
0.2%
300-30010
 
0.2%
75-3004
 
0.1%
(Missing)194
 
3.9%

Length

2021-09-12T14:16:24.312085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:24.370174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
20-501706
35.8%
100-2001590
33.3%
40-1001210
25.4%
100-400239
 
5.0%
200-40011
 
0.2%
300-30010
 
0.2%
75-3004
 
0.1%

Most occurring characters

ValueCountFrequency (%)
014450
48.2%
-4770
 
15.9%
23307
 
11.0%
13039
 
10.1%
51710
 
5.7%
41460
 
4.9%
1210
 
4.0%
324
 
0.1%
74
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23994
80.0%
Dash Punctuation4770
 
15.9%
Space Separator1210
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014450
60.2%
23307
 
13.8%
13039
 
12.7%
51710
 
7.1%
41460
 
6.1%
324
 
0.1%
74
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-4770
100.0%
Space Separator
ValueCountFrequency (%)
1210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common29974
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014450
48.2%
-4770
 
15.9%
23307
 
11.0%
13039
 
10.1%
51710
 
5.7%
41460
 
4.9%
1210
 
4.0%
324
 
0.1%
74
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII29974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014450
48.2%
-4770
 
15.9%
23307
 
11.0%
13039
 
10.1%
51710
 
5.7%
41460
 
4.9%
1210
 
4.0%
324
 
0.1%
74
 
< 0.1%

EEA_PolicyYear
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
2006
4964 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters19856
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006
2nd row2006
3rd row2006
4th row2006
5th row2006

Common Values

ValueCountFrequency (%)
20064964
100.0%

Length

2021-09-12T14:16:24.484262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:24.520387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
20064964
100.0%

Most occurring characters

ValueCountFrequency (%)
09928
50.0%
24964
25.0%
64964
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number19856
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09928
50.0%
24964
25.0%
64964
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common19856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09928
50.0%
24964
25.0%
64964
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09928
50.0%
24964
25.0%
64964
25.0%

SYS_Renewed
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
4411 
False
553 
ValueCountFrequency (%)
True4411
88.9%
False553
 
11.1%
2021-09-12T14:16:24.542115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

SYS_New_Business
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4499 
True
465 
ValueCountFrequency (%)
False4499
90.6%
True465
 
9.4%
2021-09-12T14:16:24.564548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Annual_Premium
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1166
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.5198791
Minimum0.6
Maximum2336.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:24.611535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile107.12
Q1160.06
median328.6
Q3444.14
95-th percentile665.68
Maximum2336.24
Range2335.64
Interquartile range (IQR)284.08

Descriptive statistics

Standard deviation211.4930709
Coefficient of variation (CV)0.6284712553
Kurtosis9.731675721
Mean336.5198791
Median Absolute Deviation (MAD)147.34
Skewness1.991912845
Sum1670484.68
Variance44729.31903
MonotonicityNot monotonic
2021-09-12T14:16:24.693616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.8439
 
0.8%
127.234
 
0.7%
124.0234
 
0.7%
125.0833
 
0.7%
130.3831
 
0.6%
133.5631
 
0.6%
129.3229
 
0.6%
138.8627
 
0.5%
137.827
 
0.5%
139.9226
 
0.5%
Other values (1156)4653
93.7%
ValueCountFrequency (%)
0.61
< 0.1%
0.641
< 0.1%
0.881
< 0.1%
1.241
< 0.1%
2.111
< 0.1%
3.711
< 0.1%
7.711
< 0.1%
10.561
< 0.1%
11.431
< 0.1%
11.61
< 0.1%
ValueCountFrequency (%)
2336.241
< 0.1%
2227.061
< 0.1%
2179.361
< 0.1%
2034.141
< 0.1%
2018.241
< 0.1%
1880.441
< 0.1%
1780.81
< 0.1%
1679.041
< 0.1%
1671.621
< 0.1%
1630.961
< 0.1%

Claim_Count
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4715 
1
 
235
2
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04715
95.0%
1235
 
4.7%
214
 
0.3%

Length

2021-09-12T14:16:24.818262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-12T14:16:24.855584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04715
95.0%
1235
 
4.7%
214
 
0.3%

Most occurring characters

ValueCountFrequency (%)
04715
95.0%
1235
 
4.7%
214
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04715
95.0%
1235
 
4.7%
214
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04715
95.0%
1235
 
4.7%
214
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04715
95.0%
1235
 
4.7%
214
 
0.3%

Loss_Amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct230
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.8615431
Minimum0
Maximum297025
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:24.907389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23.1625
Maximum297025
Range297025
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4760.610938
Coefficient of variation (CV)16.95002771
Kurtosis3069.375612
Mean280.8615431
Median Absolute Deviation (MAD)0
Skewness51.00506603
Sum1394196.7
Variance22663416.5
MonotonicityNot monotonic
2021-09-12T14:16:24.980211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04715
95.0%
54.515
 
0.3%
49.054
 
0.1%
38.153
 
0.1%
10902
 
< 0.1%
2585.911
 
< 0.1%
1911.811
 
< 0.1%
436.661
 
< 0.1%
3073.81
 
< 0.1%
5195.811
 
< 0.1%
Other values (220)220
 
4.4%
ValueCountFrequency (%)
04715
95.0%
27.251
 
< 0.1%
38.153
 
0.1%
49.054
 
0.1%
54.515
 
0.3%
76.981
 
< 0.1%
103.551
 
< 0.1%
106.281
 
< 0.1%
196.761
 
< 0.1%
217.671
 
< 0.1%
ValueCountFrequency (%)
2970251
< 0.1%
68571.731
< 0.1%
68110.031
< 0.1%
57491.511
< 0.1%
545001
< 0.1%
44814.411
< 0.1%
29497.571
< 0.1%
272501
< 0.1%
23630.111
< 0.1%
22054.481
< 0.1%

Frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct31
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1582585533
Minimum0
Maximum121.9512195
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:25.053593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.85
Maximum121.9512195
Range121.9512195
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.123630073
Coefficient of variation (CV)13.41873806
Kurtosis2455.894534
Mean0.1582585533
Median Absolute Deviation (MAD)0
Skewness46.5164757
Sum785.5954585
Variance4.509804686
MonotonicityNot monotonic
2021-09-12T14:16:25.121011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
04715
95.0%
2198
 
4.0%
412
 
0.2%
111
 
0.2%
4.1356492972
 
< 0.1%
2.5960539981
 
< 0.1%
6.9637883011
 
< 0.1%
2.2578460151
 
< 0.1%
2.7670171561
 
< 0.1%
72.463768121
 
< 0.1%
Other values (21)21
 
0.4%
ValueCountFrequency (%)
04715
95.0%
111
 
0.2%
1.0766580531
 
< 0.1%
1.1851149561
 
< 0.1%
1.2898232941
 
< 0.1%
1.7217630851
 
< 0.1%
2198
 
4.0%
2.2578460151
 
< 0.1%
2.5960539981
 
< 0.1%
2.7107617241
 
< 0.1%
ValueCountFrequency (%)
121.95121951
< 0.1%
72.463768121
< 0.1%
26.315789471
< 0.1%
14.144271571
< 0.1%
10.060362171
< 0.1%
8.2987551871
< 0.1%
6.9637883011
< 0.1%
6.9444444441
< 0.1%
5.4854635221
< 0.1%
4.1605991261
< 0.1%

Severity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct230
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.4956577
Minimum0
Maximum297025
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:25.192821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23.1625
Maximum297025
Range297025
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4737.823352
Coefficient of variation (CV)17.38678477
Kurtosis3128.941195
Mean272.4956577
Median Absolute Deviation (MAD)0
Skewness51.69328358
Sum1352668.445
Variance22446970.12
MonotonicityNot monotonic
2021-09-12T14:16:25.265722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04715
95.0%
54.515
 
0.3%
49.054
 
0.1%
38.153
 
0.1%
10902
 
< 0.1%
5551.841
 
< 0.1%
436.661
 
< 0.1%
3073.81
 
< 0.1%
5195.811
 
< 0.1%
27.251
 
< 0.1%
Other values (220)220
 
4.4%
ValueCountFrequency (%)
04715
95.0%
27.251
 
< 0.1%
38.153
 
0.1%
49.054
 
0.1%
51.7751
 
< 0.1%
53.141
 
< 0.1%
54.515
 
0.3%
76.981
 
< 0.1%
159.461
 
< 0.1%
196.761
 
< 0.1%
ValueCountFrequency (%)
2970251
< 0.1%
68571.731
< 0.1%
68110.031
< 0.1%
57491.511
< 0.1%
545001
< 0.1%
44814.411
< 0.1%
272501
< 0.1%
23630.111
< 0.1%
22054.481
< 0.1%
15245.031
< 0.1%

Loss_Ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct231
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.151815068
Minimum0
Maximum1121.88
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2021-09-12T14:16:25.343668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0595
Maximum1121.88
Range1121.88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.36932954
Coefficient of variation (CV)21.1573283
Kurtosis1806.688373
Mean1.151815068
Median Absolute Deviation (MAD)0
Skewness40.7590306
Sum5717.61
Variance593.8642223
MonotonicityNot monotonic
2021-09-12T14:16:25.417071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04715
95.0%
0.165
 
0.1%
0.133
 
0.1%
0.833
 
0.1%
2.82
 
< 0.1%
2.582
 
< 0.1%
5.042
 
< 0.1%
0.082
 
< 0.1%
5.392
 
< 0.1%
3.182
 
< 0.1%
Other values (221)226
 
4.6%
ValueCountFrequency (%)
04715
95.0%
0.072
 
< 0.1%
0.082
 
< 0.1%
0.091
 
< 0.1%
0.121
 
< 0.1%
0.133
 
0.1%
0.141
 
< 0.1%
0.151
 
< 0.1%
0.165
 
0.1%
0.191
 
< 0.1%
ValueCountFrequency (%)
1121.881
< 0.1%
1112.871
< 0.1%
475.691
< 0.1%
262.351
< 0.1%
212.761
< 0.1%
148.871
< 0.1%
136.261
< 0.1%
115.521
< 0.1%
113.51
< 0.1%
78.91
< 0.1%

Interactions

2021-09-12T14:15:47.623884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:47.694232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:47.756845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:47.830260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:47.892914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:47.955300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.020725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.088498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.151138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.214626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.283551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.348461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.413014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.477704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.537392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.604035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.667877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.731186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.794077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.855424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.915866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:48.976176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.043229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.104414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.168384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.234898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.301940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.365090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.428625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.493221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.558388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.628158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.693537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.753871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.814560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.879508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:15:49.949905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-09-12T14:16:05.657667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:05.730612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:05.801820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:05.884057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:05.962150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.040925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.113689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.186941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.258495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.327861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.388109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.451522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.520809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.583328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.646913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.712768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.779078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.842125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.907387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:06.973254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.044560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.111573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.176916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.236690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.296878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.596130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.659630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.728353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.790174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.849603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.909840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:07.987734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.054466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.115745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.180536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.264557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.328026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.391942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.456652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.525208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.590422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.654957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.714758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.774488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.838036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.900352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:08.964552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.025905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.090506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.155676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.226782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.293297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.359675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.429465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.500511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.568350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.636845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.710657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.780838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.851212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.921792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:09.986995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.052661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.121643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.193688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.261972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.328386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.391716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.455826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.527458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.592832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.658823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.728372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.800592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.868036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:10.935490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.003881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.073687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.144720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.216798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.286914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.351150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.419068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.486389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.554124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.621052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.687167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.752103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.828109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.894913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:11.962569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.033857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.105198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.172507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.240564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.309388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.379146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.448803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.518266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.582540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.646937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.714922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.782213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.849969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.920975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:12.982506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.044296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.111734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.174506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.237805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.304134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.371817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.620801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.687475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.754284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.846323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.914836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:13.982865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:14.045621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:14.109610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:14.175276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:14.239951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T14:16:14.305971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-12T14:16:25.533444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-12T14:16:25.854243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-12T14:16:26.168258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-12T14:16:26.585496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-12T14:16:28.218216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-12T14:16:14.601431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-12T14:16:15.458333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-12T14:16:15.578019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PolicyNoPolicy_CompanyPolicy_Installment_TermPolicy_Billing_CodePolicy_Method_Of_PaymentPolicy_Reinstatement_Fee_IndicatorPolicy_Zip_Code_Garaging_LocationVehicle_TerritoryVehicle_Make_YearVehicle_Make_DescriptionVehicle_PerformanceVehicle_New_Cost_AmountVehicle_SymbolVehicle_Number_Of_Drivers_AssignedVehicle_UsageVehicle_Miles_To_WorkVehicle_Days_Per_Week_DrivenVehicle_Annual_MilesVehicle_Anti_Theft_DeviceVehicle_Passive_RestraintVehicle_Age_In_YearsVehicle_Med_Pay_LimitVehicle_Bodily_Injury_LimitVehicle_Physical_Damage_LimitVehicle_Comprehensive_Coverage_IndicatorVehicle_Comprehensive_Coverage_LimitVehicle_Collision_Coverage_IndicatorVehicle_Collision_Coverage_DeductibleDriver_TotalDriver_Total_MaleDriver_Total_FemaleDriver_Total_SingleDriver_Total_MarriedDriver_Total_Related_To_Insured_SelfDriver_Total_Related_To_Insured_SpouseDriver_Total_Related_To_Insured_ChildDriver_Total_Licensed_In_StateDriver_Minimum_AgeDriver_Maximum_AgeDriver_Total_Teenager_Age_15_19Driver_Total_College_Ages_20_23Driver_Total_Young_Adult_Ages_24_29Driver_Total_Low_Middle_Adult_Ages_30_39Driver_Total_Middle_Adult_Ages_40_49Driver_Total_Adult_Ages_50_64Driver_Total_Senior_Ages_65_69Driver_Total_Upper_Senior_Ages_70_plusVehicle_Youthful_Driver_IndicatorVehicle_Youthful_Driver_Training_CodeVehicle_Youthful_Good_Student_CodeVehicle_Driver_PointsVehicle_Safe_Driver_Discount_IndicatorEEA_Liability_Coverage_Only_IndicatorEEA_Multi_Auto_Policies_IndicatorEEA_Policy_Zip_Code_3EEA_Policy_TenureEEA_Agency_TypeEEA_Packaged_Policy_IndicatorEEA_Full_Coverage_IndicatorEEA_Prior_Bodily_Injury_LimitEEA_PolicyYearSYS_RenewedSYS_New_BusinessAnnual_PremiumClaim_CountLoss_AmountFrequencySeverityLoss_Ratio
0164562033Standard6Direct Billed to InsuredPre-paidN42602311990DODG CARAVAN SEStandard-131Pleasure-15UnknownNot ApplicableN9100025-5025000N-1N-1101010101616100000100NNot ApplicableNot Eligible for Good Student Credit0YYY42616.1HybridNN20-502006YN111.3000.000.00.000.00
1165119133Standard6Direct Billed to InsuredPre-paidN42857352001BUIK REG LS-LSEStandard-1101Pleasure-15UnknownNot ApplicableY6100025-5025000N-1Y250101010101555500000100NNot ApplicableNot Eligible for Good Student Credit0NNY42816.5StandardYY20-502006YN408.1000.000.00.000.00
2165166239Standard6Direct Billed to InsuredPre-paidN42980301977FORD TRUCKStandard-1599Farm-15UnknownNot ApplicableN91000250-500100000N-1N-1110010001474700001000NNot ApplicableNot Eligible for Good Student Credit0YYY42923.0Non-standardNN100-4002006YN125.0800.000.00.000.00
3165198832Standard6Direct Billed to InsuredInstallmentN43050352002TYTA TUNDRA SR5Standard-11599Pleasure-15UnknownNot ApplicableY51000100-30050000N-1Y250110011001373700010000NNot ApplicableNot Eligible for Good Student Credit0YNY43016.0HybridYY100-2002006YN554.3800.000.00.000.00
4165319534Standard6Direct Billed to InsuredPre-paidN42496351992CHEV CAMARO RSSports-1161Pleasure-15UnknownNot ApplicableN9200025-5025000N-1N-1211021102485300001100NNot ApplicableNot Eligible for Good Student Credit0YYY42416.5Non-standardNN20-502006YN129.3200.000.00.000.00
5165355034Standard6Direct Billed to InsuredInstallmentN42361301992FORD RANGERStandard-1599Work155UnknownNot ApplicableN9100025-5025000N-1Y500110011001393900010000NNot ApplicableNot Eligible for Good Student Credit0YNY42316.5HybridYY20-502006YN279.8400.000.00.000.00
6165386232Standard6Direct Billed to InsuredPre-paidN42357371955CHEV BELAIR 2DRStandard-131Pleasure-15UnknownNot ApplicableN9100050-10050000N-1N-1110011001555500000100NNot ApplicableNot Eligible for Good Student Credit0YYY42316.0HybridNN40-1002006NN142.0400.000.00.000.00
7165708632Preferred6Direct Billed to InsuredPre-paidN42916351972CHEV C-10 P-UStandard-141Work15UnknownNot ApplicableN9100025-5025000N-1Y500110011001505000000100NNot ApplicableNot Eligible for Good Student Credit0YNY42916.0StandardNY20-502006YN248.0400.000.00.000.00
8165951132Standard6Direct Billed to InsuredPre-paidN43155311989CHEV PICKUP1500Standard-1111Work225UnknownNot ApplicableN9500025-5025000N-1N-1211021102535600000200NNot ApplicableNot Eligible for Good Student Credit0YNY43116.0Non-standardYN20-502006YN210.9400.000.00.000.00
9165971032Standard6Direct Billed to InsuredInstallmentN43046352003HOND ACCORD LXStandard-1111Work145UnknownPassive Disabling-Vehicle RecoveryY41000100-300100000N-1Y500101011001363600010000NNot ApplicableNot Eligible for Good Student Credit0YNY43016.0StandardNY100-2002006YN432.4812770.882.02770.886.41

Last rows

PolicyNoPolicy_CompanyPolicy_Installment_TermPolicy_Billing_CodePolicy_Method_Of_PaymentPolicy_Reinstatement_Fee_IndicatorPolicy_Zip_Code_Garaging_LocationVehicle_TerritoryVehicle_Make_YearVehicle_Make_DescriptionVehicle_PerformanceVehicle_New_Cost_AmountVehicle_SymbolVehicle_Number_Of_Drivers_AssignedVehicle_UsageVehicle_Miles_To_WorkVehicle_Days_Per_Week_DrivenVehicle_Annual_MilesVehicle_Anti_Theft_DeviceVehicle_Passive_RestraintVehicle_Age_In_YearsVehicle_Med_Pay_LimitVehicle_Bodily_Injury_LimitVehicle_Physical_Damage_LimitVehicle_Comprehensive_Coverage_IndicatorVehicle_Comprehensive_Coverage_LimitVehicle_Collision_Coverage_IndicatorVehicle_Collision_Coverage_DeductibleDriver_TotalDriver_Total_MaleDriver_Total_FemaleDriver_Total_SingleDriver_Total_MarriedDriver_Total_Related_To_Insured_SelfDriver_Total_Related_To_Insured_SpouseDriver_Total_Related_To_Insured_ChildDriver_Total_Licensed_In_StateDriver_Minimum_AgeDriver_Maximum_AgeDriver_Total_Teenager_Age_15_19Driver_Total_College_Ages_20_23Driver_Total_Young_Adult_Ages_24_29Driver_Total_Low_Middle_Adult_Ages_30_39Driver_Total_Middle_Adult_Ages_40_49Driver_Total_Adult_Ages_50_64Driver_Total_Senior_Ages_65_69Driver_Total_Upper_Senior_Ages_70_plusVehicle_Youthful_Driver_IndicatorVehicle_Youthful_Driver_Training_CodeVehicle_Youthful_Good_Student_CodeVehicle_Driver_PointsVehicle_Safe_Driver_Discount_IndicatorEEA_Liability_Coverage_Only_IndicatorEEA_Multi_Auto_Policies_IndicatorEEA_Policy_Zip_Code_3EEA_Policy_TenureEEA_Agency_TypeEEA_Packaged_Policy_IndicatorEEA_Full_Coverage_IndicatorEEA_Prior_Bodily_Injury_LimitEEA_PolicyYearSYS_RenewedSYS_New_BusinessAnnual_PremiumClaim_CountLoss_AmountFrequencySeverityLoss_Ratio
4954381020700Standard6Direct Billed to InsuredPre-paidN43169302001VLKS JETTA GLSStandard-1161Work125UnknownPassive Disabling-Vehicle RecoveryY71000250-500250000N-1Y500101001001353500010000NNot ApplicableNot Eligible for Good Student Credit0NNY4310.0StandardYY100-4002006YY493.9600.00.00.00.0
4955381039600Standard6Direct Billed to InsuredPre-paidN43065271982DODG D50Standard-181Pleasure-15UnknownNot ApplicableN910000250-500100000N-1N-1101011001454500001000NNot ApplicableNot Eligible for Good Student Credit0YYY4300.0HybridNN100-4002006YY192.9200.00.00.00.0
4956381040600Preferred6Direct Billed to InsuredInstallmentN42846351999LINC NAVIGATORStandard-1161Pleasure-15UnknownPassive Disabling-Vehicle RecoveryY95000NaN-1Y75000Y500110001001686800000010NNot ApplicableNot Eligible for Good Student Credit3NNY4280.0PreferredYYNaN2006NY512.6100.00.00.00.0
4957381052900Standard6Direct Billed to InsuredPre-paidN42385351997TYTA CAMRYStandard-11199Work105UnknownNot ApplicableY91000050-10035000N-1N-1110011001454500001000NNot ApplicableNot Eligible for Good Student Credit0YYY4230.0PreferredNN40-1002006NY84.8600.00.00.00.0
4958381080800Standard6Direct Billed to InsuredInstallmentN42471321999MERC SABLELS-PRStandard-1102Pleasure-15UnknownNot ApplicableY9200025-5025000N-1Y500211101012205801000100YWithout Driver TrainingNot Eligible for Good Student Credit0NNN4240.0PreferredNY20-502006NY189.9400.00.00.00.0
4959381137600Standard6Direct Billed to InsuredInstallmentN42643362001PONT GR PRIX SEStandard-1111Pleasure-15UnknownPassive Disabling-Vehicle RecoveryY7-125-5025000N-1N-1101010101666600000010NNot ApplicableNot Eligible for Good Student Credit0NYY4260.0Non-standardNN20-502006YY140.9800.00.00.00.0
4960381140200Standard6Direct Billed to InsuredInstallmentN43066272007FORD F-250 SUPEStandard-1171Pleasure-15UnknownPassive Disabling-Vehicle RecoveryY1100025-5025000N-1Y500101101001242400100000YWith or Without Driver TrainingNot Eligible for Good Student Credit0YNY4306.0HybridYY20-502006YY594.6600.00.00.00.0
4961381148600Standard6Direct Billed to InsuredInstallmentN42694361998JEEP CHEROKEEStandard-1111Pleasure-15UnknownNot ApplicableY9-150-10035000N-1Y500101101001464600001000NNot ApplicableNot Eligible for Good Student Credit1NNY4260.0Non-standardYY40-1002006YY197.2200.00.00.00.0
4962381184700Standard6Direct Billed to InsuredPre-paidN42498351999HOND CR-V EXStandard-11199Pleasure-15UnknownNot ApplicableY9-1NaN-1Y300000Y500211021102494900002000NNot ApplicableNot Eligible for Good Student Credit0NNY4240.0HybridYYNaN2006NY358.2400.00.00.00.0
4963381258900Standard6Direct Billed to InsuredPre-paidN42891351999FORD F250 SPDTYStandard-11799Pleasure-15UnknownNot ApplicableY91000NaN-1Y100000Y500110101001353500010000NNot ApplicableNot Eligible for Good Student Credit0YNY4280.0PreferredYYNaN2006YY484.4200.00.00.00.0